System, method and computer-accessible medium for real time imaging using a portable device

ABSTRACT

Exemplary system, method and computer-accessible medium for selecting at least one location of (i) at least one receiver or transceiver or (ii) at least one transmitter or transceiver can be provided. For example, it is possible to facilitate a receipt, from the at least one transmitter or transceiver, of a plurality of signals by the receiver(s) or transceiver(s). Each of the signals has a multipath component. Then, it is possible to determine time of flight (ToF) information and angle of arrival (AoA) information of the multipath components present in the signals. Further, it is possible to determine one or more possible locations of (i) the receiver(s) or transceiver(s) or (ii) the transmitter(s) or transceiver(s) based on the ToF information, the AoA information, and a model of physical surroundings. The location(s) of (i) the receiver(s) or transceiver(s), or (ii) the transmitter(s) or transceiver(s) can be selected based on the one or more possible locations.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation-in-part, relates to and claimspriority from U.S. patent application Ser. No. 16/422,517, filed on May24, 2019, and also relates to and claims priority from U.S. PatentApplication Nos. 63/148,103, filed on Feb. 10, 2021, 62/675,869, filedon May 24, 2018, 62/852,053, filed on May 23, 2019, and, the entiredisclosures of all of which are incorporated herein by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under Grant Nos. 1909206and 2037845, awarded by the National Science Foundation. The governmenthas certain rights in the invention.

FIELD OF THE DISCLOSURE

The present disclosure relates imaging, and determining locations andpresence and movement of items, individuals, or mobile devices morespecifically, to exemplary embodiments of an exemplary system, methodand computer-accessible medium for real time imaging of the environmentand position location of a mobile or portable (e.g., moveable orattachable or handheld) device, with the assistance of one or moreadditional wireless devices, which may include one or more portabledevices, base stations (BS) or Wi-Fi hotspots.

BACKGROUND INFORMATION

Today, many people use their smartphone camera to assist in the capturepictures of places that can be hard for them to see. For example, takinga photo of electronics wiring behind a hard to reach desk, or taking aphoto in a cabinet that can be above the person's eyesight, facilitatesthe user to view a photo of the physical environment, without having toreach their own head behind the desk or to climb on a chair to see thecabinet's contents with their own eyes. To many, this can be asurprising and non-intuitive use of the smartphone camera that might nothave been contemplated a decade ago. Nevertheless, it shows how handheldcommunication devices can be used for things beyond communication.

Radio frequencies (“RF”) and/or Wi-Fi signals can be used to discernobject locations in three-dimensional (“3-D”) space, using sensing andpredictive approaches based on the signals. Position location based onradio signal strength indication (“RSSI”) can be used, but these methodssuffer gross inadequacies due to limited RF bandwidth and without thehigh resolution that directional multi-element antenna arrays canprovide. Additionally, physical obstructions in the environment cancause the distance-dependent degradation in RSSI to deviate from themean path loss predicted by path loss models, leading to furtherinaccuracies in position location estimates.

Using various radar technologies, which can use a wider signalingbandwidth, it can be possible to determine smaller distance differencesin the measurement of a returning radar images. In radio propagationchannel sounding, a wider RF signaling bandwidth can lead to greatertemporal resolution on the received probe signal when in a bi-staticradar configuration.

Thus, it may be beneficial to provide an exemplary system, method, andcomputer-accessible medium for real time imaging and position locationusing a mobile or portable (e.g., moveable or attachable or handheld)device which can overcome at least some of the deficiencies describedherein above.

SUMMARY OF EXEMPLARY EMBODIMENTS

The present application overcomes the deficiencies in the prior art, andfurther overcomes the issue of objects or people moving around which canfurther degrade inaccuracies by prior art systems that rely upon staticor old information. An exemplary system, method and computer-accessiblemedium for generating an image(s) or a video(s) or machine readablerepresentations or renderings of an environment(s), which can include,for example, generating a first radiofrequency (RF) radiation based onbeam steering or frequency sweeping using a wireless transmitter,providing the first mmWave RF radiation to the environment(s),receiving, at the wireless receiver, a second mmWave RF radiation fromthe environment(s) that can be based on the first mmWave RF radiation,and generating the image(s) or the video(s) based on the second mmWaveRF radiation. The second mmWave RF radiation may be the result of thefirst mmWave RF radiation suffering one or more reflections orscattering events off materials in the environment. In some exemplaryembodiments of the present disclosure, the wireless receiver can receivemore than one RF radiations from the environment based on the firstmmWave RF radiation from the wireless transmitter. The multiple RFradiations, called multipath components, can assist in the positionlocation of the mobile device. In some exemplary embodiments of thepresent disclosure, the wireless transmitter and receiver may be thesame wireless device. In some exemplary embodiments, the transmitter andreceiver may be separate wireless devices.

In some exemplary embodiments of the present disclosure, the video(s)can be a real-time video of the environment(s). The first mmWave RFradiation can have a frequency between about 200 MHz to about 3 THz.Information related to a phase(s) of the second mmWave RF radiation, atime of arrival of the second mmWave RF radiation, a relative time ofarrival of the second mmWave RF radiation, or an angle of arrival of thesecond mmWave RF radiation can be determined. The image(s) or thevideo(s) can be determined based on the information. A location orposition of objects in the environment can be determined, which caninclude (i) obstructions, (ii) walls, (iii) objects of interest, or (iv)people. The position of the wireless transmitter and/or wirelessreceiver can be determined based on the information related to aphase(s) of the second mmWave RF radiation, a time of arrival of thesecond mmWave RF radiation, a relative time of arrival of the secondmmWave RF radiation, or an angle of arrival of the second mmWave RFradiation, using pictures or videos of the environment.

In certain exemplary embodiments of the present disclosure, a movementof an object(s) in the environment and the wireless transmitter and/orwireless receiver can be tracked based on the second mmWave RF radiationor a location of the wireless transmitter and/or wireless receiver canbe determined based on the second mmWave RF radiation. Informationregarding the second mmWave RF radiation can be transmitted to a furtherdevice, and the image(s) or the video(s) can be received from thefurther device. The first mmWave RF radiation can be generated using anadaptive antenna array, where the adaptive antenna array includes one ofa digital antenna array, an analog antenna array, or a hybrid antennaarray. A direction of transmission of the adaptive antenna array can bemodified based on the environment(s). The image(s) or the video(s) canbe generated based on the multipath components using a machine learningprocedure.

In some exemplary embodiments of the present disclosure, the firstmmWave RF radiation can be pulsed, spread over a bandwidth, ordiscretized over a plurality of individual frequencies. A location of astud(s) in a wall(s) can be determined based on the second mmWave RFradiation. A map(s) of the environment(s) can be generated based on thesecond mmWave RF radiation or it can be received, wherein the map(s)includes a floor plan, locations of walls or locations of objects. Thereceived map(s) can be generated by a cloud server. A phase(s) of themultipath components can be determined and a distance between thewireless transmitter(s) and a receiving device can be determined basedon the phase(s). A phase ambiguity in phase(s) of the multipathcomponents can be corrected for. In other embodiments of the presentdisclosure, the distance may be determined based on the time(s) offlight of the RF radiation. A scattering pattern(s) of different anglesof receipt of the multipath components can be determined by a receivingdevice.

Additionally, an exemplary wireless transmitter can be provided, whichcan include, for example, an antenna array(s), and a computer hardwarearrangement configured to generate radiofrequency (RF) radiation usingthe antenna array(s), provide the first mmWave RF radiation to anenvironment(s), receive, using the antenna array(s), a multipathcomponents from the environment(s) that can be based on the first mmWaveRF radiation, and generate the image(s) or the video(s) based on thesecond mmWave RF radiation.

In some exemplary embodiments of the present disclosure, the video(s)can be a real-time video of the environment(s). The first mmWave RFradiation can have a frequency between about 200 MHz to about 3000 GHz.Information related to a phase(s) of the second mmWave RF radiation, atime of arrival of the second mmWave RF radiation, a relative time ofarrival of the second mmWave RF radiation, or an angle of arrival of thesecond mmWave RF radiation can be determined. The image(s) or thevideo(s) can be determined based on the information. A presence or alocation of an object(s) in the environment(s) can be determined basedon the second mmWave RF radiation.

In certain exemplary embodiments of the present disclosure, a movementof an object(s) in the environment can be tracked based on the secondmmWave RF radiation. Information regarding the second mmWave RFradiation can be transmitted to a further device, and the image(s) orthe video(s) can be received from the further device. The first mmWaveRF radiation can be generated using an adaptive antenna array, where theadaptive antenna array includes one of a digital antenna array, ananalog antenna array, or a hybrid antenna array. A direction oftransmission of the adaptive antenna array can be modified based on theenvironment(s). The image(s) or the video(s) can be generated based onthe second mmWave RF radiation using a machine learning procedure.

In some exemplary embodiments of the present disclosure, the videos canbe generated by light detection and ranging techniques (e.g., LIDAR),wherein a 2D or 3D model of the environment can be created.

In some exemplary embodiments of the present disclosure, a pre-existingmap of the environment can exist. The pre-existing map, for example, canbe drawn in a computer-aided design (CAD) software application, handdrawn, or floorplans or blueprints of the building. The pre-existing mapof the environment can be directly used for localization of the mobiledevice.

In some exemplary embodiments of the present disclosure, computingcapabilities of the wireless transmitter and/or wireless receiver canfacilitate mapping and ray tracing in real time. In some exemplaryembodiments of the present disclosure, the wireless transmitter and/orreceiver can generate a map of the environment on the fly or have mapsloaded within, thereby facilitating map-based localization algorithmsthat exploit real-time multipath propagation. In some exemplaryembodiments of the present disclosure, the augmentation of human andcomputer vision can allow users to see in the dark and see throughwalls. In some exemplary embodiments of the present disclosure, thewireless transmitter and/or receiver can download or generate a map ofthe environment on the fly and “see in the dark”.

According to some exemplary embodiments of the present disclosure, thewireless transmitter and/or receiver can behave similar to a radar,e.g., measuring the distances of prominent features in the environment,such as walls, doors, and other obstructions. Additionally, reflectionsand scattering off walls can facilitate wireless transmitter and/orreceiver(s) to view objects around corners or behind walls, asillustrated in, e.g., FIG. 2. In some exemplary embodiments of thepresent disclosure, for ranging measurements, a radar can operate in thepulsed radar mode, wherein the radar can transmit a single pulse, switchfrom transmit to receive mode, and wait for the echo back from theobject that is to be range-estimated. However, due to constraints onswitching speed, e.g., objects at a sufficient distance from the usermay be ranged. For example, an mmWave phased array with a TX-RXswitching time of ˜100 ns may not range objects closer than 50 ft(electromagnetic waves travel 1 ft/ns). To range closer objects, a UEmay simultaneously transmit and receive the radar signal, operating inthe full duplex mode, requiring TX-RX isolation.

An exemplary system, method and computer-accessible medium for selectingat least one location of (i) at least one receiver or transceiver or(ii) at least one transmitter or transceiver, can include, for example,facilitating a receipt, from the at least one transmitter ortransceiver, of a plurality of signals by the at least one receiver ortransceiver, wherein each of the signals may have a multipath component;determining time of flight (ToF) information and angle of arrival (AoA)information of the multipath components present in the signals;determining one or more possible locations of (i) the at least onereceiver or transceiver or (ii) the at least one transmitter ortransceiver based on the ToF information, the AoA information, and amodel of physical surroundings; and selecting the at least one locationof (i) the at least one receiver or transceiver or (ii) the at least onetransmitter or transceiver based on the one or more possible locations.

In some exemplary embodiments of the present disclosure, the pluralityof signals can be radiofrequency (RF) signals. In some exemplaryembodiments of the present disclosure, the RF signals can be millimeterwave (mmWave) signals. In some exemplary embodiments of the presentdisclosure, the plurality of signals can be at least one of (i) acousticsignals, (i) audio signals, (iii) optical signals, or (iv) sonarsignals.

In some exemplary embodiments of the present disclosure, an exemplarymodel of the physical surroundings can be generated using at least oneof: one or more video recordings of an environment obtained using avisible-light camera, one or more pictures of the environment obtainedusing the visible-light camera, one or more light detection and ranging(LIDAR) techniques to generate a 2D model or a 3D model of theenvironment, a radiofrequency (RF) radar, a computer-aided design (CAD)software application, a hand drawing, or floorplans or blueprints of abuilding.

In some exemplary embodiments of the present disclosure, at least one ofthe signals can be provided at least one of (i) at a frequency in arange of approximately 200 MHz to 3 THz, or (ii) with a bandwidth ofapproximately 100 MHz to 10 GHz. In some exemplary embodiments of thepresent disclosure, the determination of the one or more possiblelocations can be performed by comparing the at least one possiblelocation with the ToF information and the AoA information. In someexemplary embodiments of the present disclosure, the determination ofthe one or more possible locations can be performed using at least onesite-specific computer rendered simulation at least one of: inreal-time, by a cloud server, on the at least one receiver ortransceiver, or on the at least one transmitter or transceiver. In someexemplary embodiments of the present disclosure, the at least onetransmitter or transceiver is a portable device, base station, or a wifihotspot and at least one receiver or transceiver is a mobile or portable(e.g. moveable or attachable or handheld) device.mobile or portable(e.g., moveable or attachable or handheld).

In some exemplary embodiments of the present disclosure, the method andcomputer-accessible medium can further provide for facilitating acooperative localization as a function of the determination of the atleast one possible location. In some exemplary embodiments of thepresent disclosure, the model of the physical surroundings can bedetermined prior to facilitating the reception of at least one of thesignals. In some exemplary embodiments of the present disclosure, atleast one of (i) the at least one receiver or transceiver or (ii) the atleast one transmitter or transceiver can be movable or fixed to aspecified location. In some exemplary embodiments of the presentdisclosure, the determination of the one or more possible locations canbe performed by a computer arrangement which can be at least one of (i)a fixed or mobile system provided at a wireless transmitter, (ii) afixed or mobile system provided at a wireless receiver, or (iii) a cloudcomputing system.

In some exemplary embodiments of the present disclosure, the method andcomputer-accessible medium can further provide for determining at leastone of a position, a velocity or an acceleration of at least one of (i)the at least one receiver or transceiver or (ii) the at least onetransmitter or transceiver using, for example, a Kalman filter, anextended Kalman filter, or a particle filter.

In some exemplary embodiments of the present disclosure, the method andcomputer-accessible can further provide for selecting of the at leastone location of the at least one receiver or transceiver can be based onat least one of: a least-squares metric, or clustering the one or morepossible locations and selecting a cluster containing a maximum numberof the one or more possible locations.

In some exemplary embodiments of the present disclosure, the method andcomputer-accessible medium can further provide for determining a carrierphase of at least one of the multipath components, wherein the carrierphase can be used in conjunction with at least one of the AoAinformation or the ToF information of the multipath components todetermine the at least one location of at least one of (i) the at leastone receiver or transceiver or (ii) the at least one transmitter ortransceiver.

In some exemplary embodiments of the present disclosure, the AoAinformation can be determined using a phased antenna array provided at alocation of at least one of (i) the at least one receiver or transceiveror (ii) the at least one transmitter or transceiver.

In some exemplary embodiments of the present disclosure, the method andcompute-accessible medium can further provide, with onboard sensors,determining at least one of: an orientation of at least one of (i) theat least one receiver or transceiver or (ii) the at least onetransmitter or transceiver, or z-coordinates of at least one of (i) theat least one receiver or transceiver or (ii) the at least onetransmitter or transceiver.

In some exemplary embodiments of the present disclosure, the onboardsensor which determines the orientation can be at least one of agyroscope or an accelerometer. In some exemplary embodiments of thepresent disclosure, the onboard sensor used to determine thez-coordinates can be a barometer.

In some exemplary embodiments of the present disclosure, the at leastone location can be selected based on a lookup table which includes theAoA information and the ToF information measured at calibrated alocation in a surveyed environment. In some exemplary embodiments of thepresent disclosure, at least one of the signals can be at least one of(i) a pulsed signal, (ii) a signals that can be spread over a bandwidth,or (iii) a signal that discretized over a plurality of individualfrequencies.

An exemplary system, method and computer-accessible medium for selectingat least one location of (i) at least one receiver or transceiver or(ii) at least one transmitter or transceiver, can include, for example,facilitating a receipt, from the at least one transmitter ortransceiver, of a plurality of signals by the at least one receiver ortransceiver, wherein each of the signals has a multipath component;determining time of flight (ToF) information and angle of arrival (AoA)information of the multipath components present in the signals;determining one or more possible locations of (i) the at least onereceiver or transceiver or (ii) the at least one transmitter ortransceiver based on the ToF information, the AoA information, and amodel of physical surroundings; and selecting the at least one locationof (i) the at least one receiver or transceiver or (ii) the at least onetransmitter or transceiver based on the one or more possible locations.

An exemplary system, method and computer-accessible medium for selectingat least one location of (i) at least one receiver or transceiver or(ii) at least one transmitter or transceiver, can include, for example,at least one processor which is configured to: determine time of flight(ToF) information and angle of arrival (AoA) information of themultipath components present in the signals, and determine one or morepossible locations of (i) the at least one receiver or transceiver or(ii) the at least one transmitter or transceiver based on the ToFinformation, the AoA information, and a model of physical surroundings,wherein the at least one location of (i) the at least one receiver ortransceiver or (ii) the at least one transmitter or transceiver isselectable based on the one or more possible locations.

These and other objects, features and advantages of the exemplaryembodiments of the present disclosure will become apparent upon readingthe following detailed description of the exemplary embodiments of thepresent disclosure, when taken in conjunction with the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Further objects, features and advantages of the present disclosure willbecome apparent from the following detailed description taken inconjunction with the accompanying Figures showing illustrativeembodiments of the present disclosure, in which:

FIG. 1 is an exemplary diagram of an exemplary portable deviceinteracting with a physical environment according to an exemplaryembodiment of the present disclosure;

FIG. 2 is an exemplary diagram of exemplary antennas implemented on theexemplary device illustrating how signals can interact with anenvironment according to an exemplary embodiment of the presentdisclosure;

FIG. 3 is an exemplary diagram illustrating how the delay in propagatingsignals can relate to distance measured for a given beamformingorientation and transmitted signal according to an exemplary embodimentof the present disclosure;

FIG. 4 is an exemplary diagram illustrating the rendering of a physicalenvironment on an exemplary handheld display according to an exemplaryembodiment of the present disclosure;

FIG. 5 is an exemplary diagram illustrating user-equipment (“UE”)measures of the relative area of arrivals according to an exemplaryembodiment of the present disclosure;

FIG. 6 is an exemplary diagram illustrating exemplary ray tracing basedon exemplary images according to an exemplary embodiment of the presentdisclosure;

FIG. 7 is an exemplary graph illustrating a comparison between measuredand predicted powers of 22 transmit (“TX”)-receive (“RX”) linksaccording to an exemplary embodiment of the present disclosure;

FIG. 8 is an exemplary floor plan map showing a floor for testing theexemplary system, method and computer-accessible medium according to anexemplary embodiment of the present disclosure;

FIG. 9 is an exemplary diagram illustrating two exemplary candidatelocations of a user according to an exemplary embodiment of the presentdisclosure;

FIG. 10 is an exemplary diagram illustrating three exemplary multipathcomponents that arrive at the user according to an exemplary embodimentof the present disclosure;

FIG. 11 is an exemplary graph illustrating exemplary cumulative densityfunctions of the positioning error according to an exemplary embodimentof the present disclosure;

FIGS. 12A and 12B are an exemplary flow diagram of a method forgenerating an image and/or a video of an environment according to anexemplary embodiment of the present disclosure;

FIG. 13 is an exemplary normalized antenna gain according to anexemplary embodiment of the present disclosure;

FIG. 14 is an exemplary TA (δ₁) that may ensure uplink frames of all UEsare aligned according to an exemplary embodiment of the presentdisclosure;

FIG. 15 is an exemplary illustration of an indication of an exemplaryRMS positioning error of the method of present disclosure according toan exemplary embodiment;

FIG. 16 is an exemplary map generated on the-the-fly using a mmWaveradar on a cell phone; and

FIG. 17 is an illustration of an exemplary block diagram of an exemplarysystem in accordance with certain exemplary embodiments of the presentdisclosure.

Throughout the drawings, the same reference numerals and characters,unless otherwise stated, are used to denote like features, elements,components, or portions of the illustrated embodiments. Moreover, whilethe present disclosure will now be described in detail with reference tothe figures, it is done so in connection with the illustrativeembodiments and is not limited by the particular embodiments illustratedin the figures and the appended claims.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The exemplary system, method, and computer-accessible medium, accordingto an exemplary embodiment of the present disclosure, can include a amobile or portable (e.g., moveable or attachable or handheld) devicethat can utilize wide bandwidths for cellular or personal/unlicensedwireless communication, and which can use those wide bandwidths toprovide real time imaging data for a user of a mobile device such thatthe radio electronics incorporated for communication can also be usedfor providing 3D imaging using wideband radar-like transmissions, andthen using the display or sensors on the hand held device to render alikeness of the image, even when the human user, itself, cannot see orpredict the surroundings of the physical environment.

Detecting the phase or the time of arrival or the relative time ofarrival or the angle of arrivals of discernable angles of radio energyand radio signatures can facilitate the determination of the presenceand location of an object, can be used to track movement of items orindividuals, and can determine relative motion and changes inorientation or position or location of the items or the individuals,without requiring any active components on the item or individual beingtracked. Additionally, the position of the mobile device can be, e.g.,determined and tracked using the time of arrival or the relative time ofarrival or the angle of arrivals of discernable angles of radio energyand radio signatures. Tracking of the position of the mobile device canbe performed, for example, by using an extended Kalman filter (EKF). Theexemplary system, method and computer-accessible medium can incorporateand/or utilize movement of the exemplary mobile device (e.g., variouspositions and angles) in order to more accurately determine the positionof the mobile device and/or generate an image of the surroundings.

The exemplary system, method, and computer-accessible medium, accordingto an exemplary embodiment of the present disclosure, can estimate thevelocity of the mobile device, for example, by measuring the dopplershift in the first RF transmission, or with onboard gyroscopes,accelerometers and other sensors on the mobile device.

Alternatively or in addition, the exemplary system, method, andcomputer-accessible medium, according to an exemplary embodiment of thepresent disclosure, can estimate the orientation of the mobile devicewith onboard sensors, e.g., gyroscopes, accelerometers. Gyroscopes canmeasure the angular velocity of the mobile device, from which theorientation of the mobile device can be obtained by integration.Accelerometers can provide an estimate of the tilt of the mobile devicewith respect to the vertical axis. The exemplary system, method, andcomputer-accessible medium, according to an exemplary embodiment of thepresent disclosure, can include and/or utilize an extended Kalman filter(EKF) which can be a recursive low pass filter that smoothens the errorin the position of the mobile device being tracked. The exemplarysystem, method, and computer-accessible medium, according to anexemplary embodiment of the present disclosure, can combine exemplarymeasurements from different sources (e.g., angular measurements,temporal measurements, GPS measurements) by the filter, while minimizingthe variance of the expected position location error.

The exemplary system, method and computer-accessible medium, accordingto an exemplary embodiment of the present disclosure, can determine theposition of one or more objects in the environment. The position can bean absolute position of the object (e.g., based on a priori knowledge ofthe environment which can have been previously determined).Alternatively, the position can be a relative position of the objectwith respect to the mobile device. For example, the distance, angle, andheight from each object can be determined relative to the mobile device.Additionally, various other suitable exemplary position/informationsensors can be utilized, which can be incorporated in the mobile device.These can include, but are not limited to barometers, gyroscopes,capacitive sensors, transducers, video camera, as well as inertialnavigations procedures. Alternatively, or in addition, a position of themobile device can be determined. Then using the a priori knowledge ofthe environment, the absolute position of each object can be determined.

After the position of each object is determined, the exemplary system,method and computer-accessible medium, according to an exemplaryembodiment of the present disclosure, can generate an image, a video ora map of the environment. For example, the relative or absolute positionof each object can be used to render the image (e.g., by providing thelocation for each object in the image). Additionally, surfacecharacteristics of each object can be utilized along with the positionto more accurately render the image, video or map. Various exemplarymachine learning procedures can be utilized to determine the type ofobject being imaged. This information can be incorporated into theimage, video, or map to provide increased accuracy. For example, byidentifying the actual object, known characteristic of the object typecan be used as parameters to render the actual object being imaged intothe image, video, or map. Further, frequency sweeping can be used tofine tune the focus of the image to be captured or determined. The userdevice can perform this using barometers, gyroscopes, capacitivesensors, and other known position location mechanisms that can beincorporated into a mobile device.

By using wireless communication spectrum that can be in the millimeterwave or even higher frequency range, for example, of about 10 GHz (e.g.,between 8 GHz to about 12 GHz) up to and including about 3000 GHz (e.g.,about 2700 GHz to about 3300 GHz)), it can be possible to use thesmaller wavelengths and greater allocated spectrum bandwidths (e.g., RFbandwidths of 2 GHz up to and including 100 GHz of width), to yieldsuper-resolution range finding, imaging, motion detection, rendering ofthe physical environment, even when there can be an obstructed view of aphysical space, or if there can be insufficient light for the human todiscern any of these kinds of cues in the environment. Additionally,certain frequency ranges can be particularly beneficial (e.g., about 80GHz to about 900 GHz, and, according to various exemplary embodiments ofthe present disclosure, frequencies below 80 GHz can be utilized aswell. The processing for such unprecedented imaging and rendering can bedone on the device (e.g., based on Moore's law—the computationalcapacity of mobile devices can grow exponentially), or can be sharedbetween the device itself and processing that can be performed remote tothe device, using the wide bandwidths of wireless networks.Additionally, exemplary computation can be performed remotely from thedevice, and sent back to the device for storage, manipulation,augmentation, or iterative cooperative processing between the device,the human user of the device, and one or more computing engines remoteto the device. Smaller wavelengths at greater wireless carrierfrequencies between about 10 GHz and 3000 GHz can facilitate the use ofphysically small adaptive antenna arrays, which can be digital, analog,or hybrid in their use of beamforming.

At various exemplary frequencies, each antenna element can be quitesmall (e.g., on the order of a wavelength or even smaller if implementedon high dielectric materials such as on-chip antenna, on substrateantennas, or antennas implemented on high epsilon circuit boards, or onthe skin of fabric of a device). At 10 GHz, the free space wavelengthcan be 30 millimeters (e.g., 3 cm), and at 3000 GHz, the free spacewavelength can be 0.1 millimeter, thus illustrating that hundreds orthousands or more antenna elements can easily fit on a mobile orportable (e.g., moveable or attachable or handheld) device at suchfrequencies, providing super angular and spatial and temporal resolutionfor a wideband signal that can be emitted by such a wireless devices.Similarly, at such high carrier frequencies, the narrowband bandwidthcan be quite large (e.g., a few percent of the carrier frequency at 3000GHz can be an astounding 90 GHz) thereby facilitating low costelectronics, many with resonant circuits, and reliable and reproducible“narrowband processing” electronics over unprecedented wide RFbandwidths about an unprecedentedly high carrier frequency, and thus canprovide extremely good precision and relative accuracy for measuringrelative distances through time delay detected from signals that can bereflected or scattered from the physical environment.

In some exemplary embodiments of the present disclosure, the shortwavelength in the mmWave frequency band can allow electrically large(but physically small) antenna arrays to be deployed at both the UE andBS. MmWave BS antenna arrays with 256 antenna elements and 32-elementmobile antenna arrays are already commercially available. Thefrequency-independent half-power beamwidth (HPBW) of a uniformrectangular array (URA) antenna with half-wavelength element spacing canbe approximately (102/N)°, where N is the number of antenna elements ineach linear dimension of the planar array, as shown in, e.g., FIG. 13.FIG. 13 illustrates an exemplary normalized antenna gain (with respectto boresight, the axis of maximum gain) of URAs with 8×8, 16×16, 32×32,and 64×64 array elements. In these exemplary embodiments, the half powerbeamwidths (HPBWs) are 12.76°, 6.34°, 3.17°, and 1.55°, respectively.

Narrower HPBWs of antenna arrays can facilitate the AoA of receivedsignals to be estimated precisely, and further signal processingprovides better accuracy. For example, the sum-and-difference for aninfrared system technique achieved sub-degree angular resolution withtwo overlapping and slightly offset antenna arrays, showing it ispossible, e.g., to very accurately detect precise AoA at UEs or BSs.

By sending a RF transmission, either pulsed, spread over bandwidth, ordiscretized over many individual frequencies, using an exemplarymodulation procedure that can be used to carry baseband signals over acarrier, the mobile or portable (e.g., moveable or attachable orhandheld) device, such as a cellphone, communicator, all-purposeelectronic wallet etc. can radiate energy in time and space, such thatreturned backscatter, reflection, and scattered signal energy radiatedby the mobile or portable (e.g., moveable or attachable or handheld)device can be processed, at the device itself or remotely at a networkcomputation site or other remote processing center, and then rendered bythe mobile or portable (e.g., moveable or attachable or handheld) devicefor the user to assimilate. Alternatively, one or more wirelesstransmitters (for example base stations, wifi hotspots, or portabledevices) could send a RF transmission, which could be processed by themobile or portable device or remotely at a network computation site orother remote processing center and then rendered by the mobile orportable (e.g., moveable or attachable or handheld) device for the userto assimilate.

The exemplary mobile or portable (e.g., moveable or attachable orhandheld) imaging device can be integrated in a cellphone, personalappliance, electronic wallet, or could be a standalone item such as awallboard stud finder found in today's hardware stores. When implementedas part of a smart phone or pocket communicator, the preferredimplementation, the device can use, for example, frequencies that arethe same, similar or different than commercial wireless frequencies usedfor cellphone or Wi-Fi or ultrawideband, or Bluetooth communication.

The exemplary mobile or portable (e.g., moveable or attachable orhandheld) device can include a viewing screen for rendering a photo ormoving image or virtual view of the physical environment for the humanuser, as well as one or more cameras, and can include augmented realityto superimpose sensed data with actual data captured by the camera(s) orrendered photo. Even without a camera or image rendering screencapability, the exemplary device can use audio tones, alerts, text, orother means to communicate sensory observations to the user.

FIG. 1 is an exemplary diagram of an exemplary portable deviceinteracting with a physical environment according to an exemplaryembodiment of the present disclosure. As shown in FIG. 1, the exemplarymobile or portable (e.g., moveable or attachable or handheld) device(e.g. 105) can send out RF signals 110 of wide bandwidth (e.g., betweenabout 2 to about 90 GHz in RF bandwidth for super resolution in space,as well as between about 6 GHz to about 3 THz), using carrierfrequencies of, for example, 10 GHz up to 3000 GHz, and through thesystematic transmission and reception of RF energy received back fromthe physical environment. The exemplary mobile or portable (e.g.,moveable or attachable or handheld) device device 105 can use theimaging data from many locations in the physical space to create animage of the physical environment. The exemplary device can use atransceiver 115, or separate receiver and transmitter 115, which can becoupled to an electronically steered antenna array consisting of one ormore antenna elements that can form beams of energy for transmission andreception.

The exemplary device can become a rendering device that can determineand show the user the physical surroundings of places that the humanuser cannot see for themselves, determining what can be behind walls120, floors 125 or objects 130, determining the environment in the dark,augmenting an existing photo or known environment from a map or pictureor past rendering stored or retrieved by the mobile or portable (e.g.,moveable or attachable or handheld) device 105. The wireless device 105can also provide computing based on sensors on the phone, or can haveassisted computing for such rendering sending to remote processing unitsthat can communicate with the exemplary mobile or portable (e.g.,moveable or attachable or handheld) device, facilitating the device toshow or store the image, and facilitating the user or the device tomanipulate, zoom, highlight, shade, reorient/tilt either on the imagedisplayed on the device in real time, or in pseudo real time withsuccessive processing on the fly on the device or with data representingthe measured sensory data and imaging sent back from the exemplarymobile or portable (e.g., moveable or attachable or handheld) device toremote computing resources that can be accessed through an existingwireless communication network.

As shown in FIG. 2, more than one antenna 205 on the mobile or portable(e.g., moveable or attachable or handheld) device 105 can form beams ofenergy for transmission and reception, and these beams can be formed inparallel, or can be formed in sequential scans in azimuth and elevation,and the beam patterns made for transmission can be course (e.g., widebeam) and electronically shifted to become more fine (e.g., narrow) inbeamwidth. Beamforming can facilitate for one main lobe, and/or manylobes, with varying degree of sidelobes, and that the antenna elementsused for imaging the environment can be the same or different orpartially shared with those used for wireless communications.

As shown in FIG. 2, either 2D azimuthal or elevation scans can beperformed using the exemplary adaptive antennas for sensing the physicalenvironment, or 3D in both azimuth and elevation can be performed, wherescans can be variably oriented based on the handheld orientation and thedesired orientation by the human user, etc. The radio signals 210 and215 emitted and received by the exemplary device can be limited by theradio transmitter power, and enhancement in signal range can be achievedthrough averaging over time, frequency, code, space, and other exemplaryprocedures, and the narrow beams provided by a large antenna array canovercome free space propagation loss in the vicinity of the handhelddevice, thereby facilitating relatively long (e.g., one to many tens ofmeters) of range in proximity of the handheld device at low batterypower levels typically used for smartphones. Different radio frequenciescan scatter, reflect, diffract, or penetrate different materials atdifferent frequencies. By using an exemplary model of suchelectromagnetic properties as a function of frequency of operation, theexemplary device can transmit and receive at many closely spaced angulardimensions to obtain get a very fine resolution model of the physicalenvironment, where the time delays of different returning signals canfacilitate computation of the physical distances associated withphysical objects in the environment. Strength of signals can degradewhen passing through walls or traveling through fog or rain,facilitating both physical objects as well as weather and atmosphericchanges to be detected and imaged.

Some frequency bands, such as 70 GHz and 140 GHz, can have lessattenuation in free space than other bands, such as 60 GHz or 380 GHz.Angular spacings can be synthesized on the transmitting phased arrayantennas using particular geometries (e.g., patch arrays, uniform lineararrays, uniform rectangular arrays, closely spaced arrays that takeadvantage of spatial relativity for improving signal to noise ratio andreducing sampling resolution requirements, conformal arrays, and on-chipand flip-chip phased arrays. The angular resolution and pointing anglescan be determined by the amplitudes and/or phases of electrical signalsapplied to the transmitting antennas, and receiving antennas, which canbe the same or different structures. Exemplary antenna architectures caninclude analog beamforming, digital beamforming, or hybrid beamforming.By transmitting a signal at different frequencies, and determining thefrequency-dependent characteristics of the channel (e.g. the air,response of objects and humans, and the frequency-dependent nature orwavelength-dependent nature of various propagation phenomena, such asBrewster angle/polarizing angle), such that different reflection orscattering mechanisms can be identified from particular objects orphenomena, the exemplary system, method and computer-accessible mediumcan learn and determine what objects can exist in the channel, and wherethey can exist relative to other objects, based on time of flight ofparticular signatures measured in the channel.

FIG. 3 shows an exemplary diagram illustrating how the transmittedsignal from the mobile or portable (e.g., moveable or attachable orhandheld) device 105 can interact with the physical environment 305,facilitating the received version of the signal to be captured by thereceiver in the mobile or portable (e.g., moveable or attachable orhandheld) device. By capturing thousands of incremental versions of theelectromagnetic responses of the physical environment 305 as a functionof position in space, and by determining signaling over many samples ofdifferent locations in space, sufficient data can be obtained in whichthe exemplary system, method and computer-accessible medium can use tointerpret the physical environment and received radio signals in orderto form a rendering of the environment on the device for the user toview and interact with. Since reflections and scattering of RF energycan “go around corners and behind walls”, and often energy can passthrough walls with various attenuation characteristics, the exemplarysystem, method and computer-accessible medium can reconstruct anaccurate estimate of the physical environment without the human userbeing able to see the environment, itself. Since RF energy can propagatewhether there can be daylight or illumination or not, the exemplarysystem, method and computer-accessible medium can be used to see in thedark.

Additionally, the exemplary system, method and computer-accessiblemedium, according to an exemplary embodiment of the present disclosure,can be used to determine fog or smoke or rain or hail, as well as thecharacteristics of building materials and human reflectivity, and canhave various attenuation and electromagnetic properties that can bemodeled and incorporated into assessing the physical environment. Use ofantenna polarization in the transmission and reception can facilitatethe determination of the physical environment as many objects can besensitive to polarization. Using previously determined versions of thephysical environment, based on the location of the device and known mapsand images for such location, the exemplary device can more easilypredict the physical environment than just through RF imaging, alone.

Exemplary antenna polarizations can be altered by applying RF energy toorthogonally fabricated antenna elements on the antenna array, or usingalternately oriented spatial arrays, for example, with different lineararrays that can be positioned in orthogonal axes on the device. Circularpolarization, either right-handed or left-handed, can be excited byusing phase delays on both horizontal and vertically oriented arrays,and circular polarization can be used to remove or attenuate variousexemplary multipath components (e.g., through reverse circularpolarization on reflection) that can bounce off floors or othersurfaces, as well as other suitable procedures. Using variouspolarizations and frequencies, and incorporating channel sounding withalternate polarizations and alternate handed circular polarization, theexemplary system, method and computer-accessible medium can identifyvarious surfaces, which can be learned over time for accurate sensing ofthe environment and creation of a map of locations of objects, items orpeople. For example, the exemplary system, method andcomputer-accessible medium, according to an exemplary embodiment of thepresent disclosure, can be used to identify different objects, people,clothing, inanimate objects, etc.

FIG. 4 illustrates how the exemplary mobile or portable (e.g., moveableor attachable or handheld) device 105 can show to a user 410 therendering of the physical environment 305, even though the user cannotsee the physical environment 305. In this exemplary scenario, a person410 can be situated behind the wall 120, and the user 405 cannot seethrough the wall 120, but the device 105 can predict and show a view ofthe person 410, relative to the physical environment 305. The exemplarymobile or portable (e.g., moveable or attachable or handheld) device 105can incorporate past views, models or augmented reality of the physicallocation of interest to the user 405. Processing utilized to constructthe image can be based on the response of objects and materials to radioimaging as a function of radio frequency (e.g., objects and people havefrequency dependent characteristics) as well as the time delays obtainedby the measurements of the hand held device indicating the relativelocation of items, in addition to expert learning and artificialintelligence that facilitates the procedures to improve over time andwith more user experience. The image that can be displayed on the handheld device can be a static picture, an image, a moving picture (e.g.video), and can be formulated based on previous photographs, images,models or maps that may have existed before the use of the device.Alternatively, the imaging device can create an image, and this imagecan be stored and used by the device, and sent to a remote storagefacility for use in a database that can build images and can continue tolearn about physical environmental modeling, for improved performance ofthe mobile or portable (e.g., moveable or attachable or handheld) deviceimaging for a population of users.

In certain exemplary embodiments of the present disclosure, mobiledevices can directly communicate with one another via device-to-device(D2D) communications, instead of communicating with the BS, fordetermining mobile device position location. In example embodiments,mobile devices may conduct range and angular measurements on each D2Dlink. In example embodiments, the relative mobile device locationinformation, extracted from the D2D link measurements, can be sent to acentral localization unit co-located at one of the serving BS or acentral server. The position of all the UEs in the network cansimultaneously be determined by nonlinear least squares (LS) estimation,wherein the positions of the UEs that jointly minimize the deviationfrom the physical angular orientation and distance-based linkconstraints can be determined.

The exemplary system, method, and computer-accessible medium, accordingto an exemplary embodiment of the present disclosure, can include anexemplary mobile or portable (e.g., moveable or attachable or handheld)device, which can be used for portraying an image or providing cuesrelating to a physical environment that cannot be visualized by a humanuser. The exemplary device can utilize one or more antennas that, forexample, can transmit and receive signals between about 10 GHz and about3000 GHz. The one or more antennas can be electronically steered to formone or more beams that can facilitate transmission and reception ofradio signals in proximity of the exemplary wireless device. The radiosignals can have a RF bandwidth of between about 2 and about 90 GHz. Theexemplary wireless device can render an image of a physical space thatmay not be seen by the naked eye of the user of the wireless device. Theimage can be compiled based on sensory data obtained by the exemplarymobile or portable (e.g., moveable or attachable or handheld) deviceusing the one or more antennas and the radio signals. The exemplarywireless device can also be used for wireless communications.

Additional antenna elements in an array can provide a greater gain inthe boresight direction and simultaneously a narrower main beamwidthlobe than an antenna with fewer active elements. Antenna arrays can useclusters of antenna elements in a hybrid fashion, or analog or digitalbeamforming can be done using many antenna elements. The wavelength atabout 73 GHz can be approximately 4.1 mm which can correspond to ahalf-wavelength antenna separation of about 2.05 mm. If a BS antennaarray can have 256 elements, where each antenna element can be a patchantenna with approximately 3 dBi of gain, it can cover an area ofapproximately 32.8 mm by 32.8 mm and results in a gain of 27 dBi (notconsidering feeder loss). Additionally, if a mobile antenna array canhave 16 elements where each antenna element can be a patch antenna with6 dBi of gain, it can cover an area of approximately 8.2 mm×8.2 mm.

Additional antenna elements can result in greater gain and tighter(e.g., narrower) beamwidths, and can impact the spatial resolution andsignal levels that can be received upon radar returns. Higherfrequencies can result in smaller wavelength and thus a greater numberof elements and greater gain can be achieved in the same physical area.Averaging the signal signatures, over time, space, and frequency canyield better signal to noise ratio (“SNR”) and deeper resolution forarriving signals.

The exemplary system, method, and computer-accessible medium, accordingto an exemplary embodiment of the present disclosure, can utilize widebandwidth measurements and array processing with small variations in theradiated major beam angle to distinguish variations in the propagationchannel over small angular separations which can map small distancesbetween impinging objects in the channel. The exemplary system, methodand computer-accessible medium can facilitate the measurement/detectionof small differential time delays, using the wideband transmissions todistinguish time delays as a function of angle. By sensing the smalldifferences in “returned echoes”, which can yield propagation distancesand their intensity, a map of the environment can be made. Additionally,e.g., machine learning procedures/systems can be used to determine abaseline set of known expected attenuations for a wide range ofenvironmental partitions.

Thus, the exemplary system, method, and computer-accessible medium, canbe used to build a map of the environment from the myriad ofmeasurements, which can improve over time. The reflected and scatteredenergy can arrive from various directions, including those that can bebehind or propagate through partitions. The exemplary system, method,and computer-accessible medium, according to an exemplary embodiment ofthe present disclosure, can learn the expected delays by understandingpropagation responses. For example, a received signature that shows adelay at about 4 ns and then a weaker one at about 8 ns can bedetermined to be a wall that can be about 2 feet away (e.g., radio wavestravel 1 foot per ns, thus a 4 ns return can mean that the signaltransmitted 2 feet and then bounced back 2 feet, indicating a 2 footdistance to the first wall, and the much weaker return at 8 feet couldbe determined to be a second wall or object behind the first wall, sincethe 8 foot signal would have to travel 4 feet (e.g., passing the firstwall and then hitting a second wall), and then reflected back 4 feet,for a total of an 8 ns delay. The amplitudes and angular values frommany closely spaced signatures with slightly different angles can beused to confirm the assumptions of the “unseen” objects while building amap of the environment that can be overlaid or augmented with a known orvisual image of the environment.

Using exemplary array processing, with controllable 2-D or 3-D antennaarray beam steering, it can be possible to launch a transmitted signal,either narrowband, wideband, or sweeping/chirp signal, that canfacilitate transmitted signal function to be launched in a particulardirection. The response from the launched signal can contain theconvolution of the launched signal with the response of the channel. Byprobing the channel with incremental changes in the spatial direction ofthe launched signal, the exemplary system, method andcomputer-accessible medium can identify variations in the response ofthe radio channel. Subtle differences in the received channel response,as a function of small change in launch angle, can be due to thevariations in propagation, and can be based on the changes in thegeometry of the impinging radio energy on various objects in thechannel. As discussed above, the frequency-dependent nature andgeometric dependencies on radio reflections, diffractions, andscatterings can be known or learned from successive transmissions atdifferent directions, and matched or estimated based on a library ofknown responses to theoretical reflection, diffraction or scattering.

The exemplary system, method and computer-accessible medium can utilizemachine learning to estimate the most likely or reasonable guesses as towhat objects can cause the variations in radio responses for variousperturbations in the launched radio signals. By using the receivedresponses and comparing the variations across minute angles, and usinggeometric models that can be iterated for different distances from atransmitter, the received signal responses, such as received amplitudeor power, phase, true time of flight, relative time delay betweensignals in a received signal response, the time delay of responses ofthe received signal, etc. can be used to determine the objects in aphysical surroundings that can cause the measured variations in receivedsignal responses as a function of angle and frequency, especially whenconsidered with measured physical response such as time of flight, andparticular signal level changes as a function of frequency and angle.

As an example, a smooth scatterer, such as a marble wall or glass, canhave a relatively simple, angle dependent reflection response over awide range of frequencies for a given polarization, and such a responsecan be known ahead of time, through computer memory (e.g., through lookup tables as defined objects), or can be learned through calibration orpretest of the exemplary system. The particular signal can be detectedby using a transmitter that transmits a signal at varying angles, and aco-located receiver can search for the reflected signal signature. Atthe particular transmitted and received angles, when the peak signalarrives from the smooth scatter, the beneficial signature can arrive andthe smooth object can be identified. There can be, e.g., a few, dozensor hundreds of particular objects used with known frequency andpolarization dependent signatures. More complex scattering andreflection can occur with rough surfaces, such as carpeting or plasterwalls, or people, but the responses over a wide range of frequencies,polarizations, and incident/departure angles can be known, as a look upor pre-loaded, or can be learned or trained in the operation of theexemplary system.

The exemplary system, method, and computer-accessible medium, accordingto an exemplary embodiment of the present disclosure, can model Dopplereffects for objects in motion, where Doppler can be a function offrequency as well as the angular orientation of the signal with theobject's direction of motion. Thus, the exemplary system, method, andcomputer-accessible medium, can accurately estimate the location (e.g.,the spatial position of a particular object with a known signature).Thus, the exemplary system, method, and computer-accessible medium, canbe used to locate the position of a particular object of interest.

Exemplary Map-Assisted Millimeter Wave Localization for AccuratePosition Location

Positioning is the determination of the location of a wirelesstransmitter and/or receiver that is fixed or moving, based on the knownlocations of other reference points. In line-of-sight (“LOS”)environments, the time of flight (“ToF”) of a signal can be used toestimate the transmitter (“TX”)—receiver (“RX”) separation distance d(e.g., sinced=c·t, where c is the speed of light and t is the ToF). Theposition of the wireless device can then be estimated by trilateration.In non-LOS (“NLOS”) environments, ranging based on ToF alone introducesa positive bias in the position estimates since the path length ofreflected multipath rays is longer than the true distance between thewireless transmitter and receiver

The ultra-wide bandwidths available at mmWave and Terahertz (“THz”)frequencies facilitate the RXs to resolve finely spaced multipathcomponents and accurately measure the ToF of the signal. The phaseaccrued by a signal in LOS is proportionate to the ToF. The phase of thereceived signal in LOS can be used to estimate the TX-RX (“TR”)separation distance in LOS at 300 GHz. The phase of a signal rotates aradians every λ meters, yet phase ambiguity arises, since signals thattraverse distances that differ by integral multiples of λ incur the samephase. Prior work (J. S. Parker, P. Mickelson, J. Yeak, K. Kremeyer, andJ. Rife, “Exploiting the Terahertz Band for Radionavigation,” Journal ofInfrared, Millimeter, and Terahertz Waves, vol. 37, no. 10, pp.1021-1042, Oct. 2016) tracked the phase of the transmitted signal at theRX and manually correcting for phase ambiguity, decimeter-level accuracyat 300 GHz can be achieved up to distances of 40 m in LOS.

A problem in determining of a mobile user's precise location fromangular measurements can be similar to the ‘three point problem’ asknown in land surveying, and can be used by electrically steerablephased array antennas in wireless systems at mmWave and THz frequencies.

FIG. 5 shows an exemplary diagram illustrating the UE measures of therelative area of arrivals according to an exemplary embodiment of thepresent disclosure. As shown therein, relative angles θ₁ and θ₂ betweenBSs are measured by the user. The unknown position of the mobile user isthen calculated (e.g., trigonometrically). Additionally, geometricobservations can illustrate that that the locus of points where BS₁ andBS₂ subtend (e.g., point 505) a fixed angle θ₁ is a circlecircumscribing the triangle formed by the BSs (e.g., taken pairwise) andthe user. The user location can correspond to the intersection of thetwo circles 510 and 515 corresponding to the two relative AoAs measuredbetween two BS pairs. The solution to the three point problem can besensitive to small errors in measured angles when either the BSs subtenda small angle at the user location, or when the observation point is onor near a circle which contains the three BSs. Look-up tables can alsobe used to localize the user, where the table stores the relative AoAsmeasured by the user at each location in the surveyed environment.

Narrow antenna beamwidths of antenna arrays at mmWave frequencies canfacilitate the user device to accurately determine the AoA. In order toimprove the AoA accuracy, two photodetectors can be placed at an offsetand mechanically rotated. The sum and difference of the signals receivedby the two photodetectors can be calculated to determine the preciseangle, even with a course beam pattern. When the sum of the signalsreceived at the two detectors exceeds a threshold and the absolute valueof the difference in the signals falls below the threshold, the beaconcan be detected to be aligned with the photodetectors. An on-the-flyangular resolution of about 0.2° can be achieved, with widebandphotodetectors, as was achieved in prior work (C. D. McGillem and T. S.Rappaport, “Infra-red location system for navigation of autonomousvehicles,” in Proceedings. 1988 IEEE International Conference onRobotics and Automation, vol. 2, Apr. 1988, pp. 1236-1238).

The exemplary sum-and-difference method can also be implemented byelectrically steering an antenna array, using adjacent antenna beams orslightly offset antenna arrays with overlapping antenna patterns. Asingle antenna can also be used to locate the AoA of the peak signalfrom the BSs by quickly dithering the antenna boresight (e.g.,electrically or mechanically). The sum and difference of the receivedsignals at successive time instants can be used in place of measurementsfrom two (or more) offset antennas.

In NLOS environments, due to specular reflections from walls andmetallic surfaces, rays do not arrive from the direction of the BS,leading to accuracy penalties, if used the AoA is used directly.Real-time electric beam steering procedures can facilitate scanning ofroom features in a matter of seconds. As a result, mobile phones may beable to generate detailed 3-D maps on the fly, or download them from thecloud. For example, cloud servers, edge servers, other base station,access points or another user on the network can be used to generate themap. Additionally, the map generation can be crowd sourced usingmultiple devices. NLOS objects (e.g., around corners) can be “viewed” byfirst rapidly scanning the environment via beam steering, in order todetermine all the reflecting obstructions in the surroundings. Thereflecting obstructions can then be distinguished from the target NLOSobject to be “viewed” by taking advantage of the fine temporalresolution at mmWave and sub-THz frequencies to create a 3-D map of thelocal environment. The 3-D maps can be utilized (e.g., in conjugationwith angle of departure (“AoD”) from the known BSs and ToF measurements)to calculate, back-solve or estimate the actual paths that the multipathcomponents take to reach the user. The exemplary paths taken by themultipath components that reach the user contain sufficient informationto localize a user in NLOS, even in the absence of LOS multipathsignals.

Exemplary 3-D mmWave Ray Tracer

Since wideband directional measurements can be expensive and timeconsuming, a ray-tracer that can be truthful to actual measurements at awide range of locations can be a powerful tool for determining positionlocation procedures, data fusion, and overall position location accuracyand sensitivity.

NYURay, a 3-D mmWave ray tracer has been developed (O. Kanhere, S. Ju,Y. Xing, and T. S. Rappaport, “Map Assisted Millimeter Wave Localizationfor Accurate Position Location,” in IEEE Global CommunicationsConference, Dec. 2019, pp. 1-6.). NYURay is a hybrid ray tracer whichcombines shooting-bouncing rays (“SBR”), with geometry-based raytracing.

A SBR ray tracer can launch rays uniformly in all directions and thentrace the path of each launched ray, as the ray interacts with variousobstructions in the environment. Each launched ray can represent acontinuous wavefront. Each ray can carry the power that can be carriedby the wavefront.

The accuracy of the AoA of rays received at the RX can depend on thenumber of rays launched from the TX. For example, rays are launched fromthe vertices of a tessellated icosahedron with tessellation factor N,since the average radial separation between two rays can be

$\frac{69{^\circ}}{N},$

for sub-degree accuracy for AoA, N>50, which can be computationallyexpensive.

Image-based ray tracing can be based on the principle that the incidentray and the reflected ray can make the same angle with the normal to theplane containing the obstruction which can be computationally mucheasier. Obstructions can be treated as infinitely long, thin, mirrorswhen using image-based ray tracing. FIG. 6 shows an exemplary diagramillustrating ray tracing based on exemplary images according to anexemplary embodiment of the present disclosure. For example, as showntherein, Im₁ is the image of RX in wall 1 and Im₂ is the images of Im₁in wall 2. The image of the RX can be taken, successively, in at most kobstructions, where k can be the maximum number of reflections a ray cango through.

If there is a large number of obstructions, the simulation run-time canbe large. Assuming that each ray can be reflected at most three times,with N obstructions in the environment, there can be

$\quad\begin{pmatrix}N \\3\end{pmatrix}$

images that need to be computed

. Although the image-based ray tracing procedure can find the directionof arrival of rays accurately, finding the reflection of the RX,recursively, from all combinations of obstructions can becomputationally expensive.

To reduce the computational overhead, NYURay uses a hybrid ray tracingprocedure. The approximate trajectories of rays that reach the RX canfirst be determined via SBR ray tracing. Once all the reflectingsurfaces in the path of a ray can be determined, image-based ray tracingcan be used to calculate the recursive reflections of the RX. The raytrajectory can be accurately calculated by connecting all the RX images.

In every direction where a ray was launched, on encountering anintersection with an obstruction, two new rays can be created - thespecular reflected ray and the transmitted ray. By Snell's Law, thereflected ray and the incident ray can form equal angles with the normalto the obstruction. The transmitted ray can be assumed to propagate inthe same direction as the incident ray. A linear model can be used tocharacterize the variation of reflection coefficient Γ with incidentangle θ_(i), based on reflection measurements. for example:

$\begin{matrix}{{{\Gamma } = {\frac{E_{r}}{E_{i}} = {{0{{.56} \cdot \theta_{i}}} + {0{.096}}}}},} & (1)\end{matrix}$

where E_(r) can be the reflected electric field, E_(i) can be theincident electric field, and θ_(i) can be the angle of incidence of theray. As a result, the reflected power P_(r)=|Γƒ²P_(i), where P_(i) canbe the power incident on the obstruction. A constant transmission lossof 7.2 dB can be assumed, based on the propagation measurements.

New source rays at each boundary can then be recursively traced in thereflection and transmission directions to the next encounteredobstruction on the propagating ray path. Path loss can be calculatedbased on the free space path loss (“FSPL”) model, with a TR separationdistance equal to the total propagated ray length. Additionally, at mostthree reflections of rays can be considered in order to reducecomputation time. The limitation on the number of reflections canfurther be justified by the observation that mmWave signals typically donot experience more than two reflections.

In prior work (G. R. MacCartney, Jr. et al., “Indoor office widebandmillimeter-wave propagation measurements and models at 28 GHz and 73 GHzfor ultra-dense 5G wireless networks,” IEEE Access, vol. 3, pp.2388-2424, Oct. 2015.), propagation measurements were conducted at 28GHz and 73 GHz at the NYU WIRELESS research center, located on the 9thfloor of 2 MTC using a 400 Megachip-per-second (“Mcps”) wideband slidingcorrelator channel sounder with high gain steerable antennas. Thedirectional antennas used in the propagation measurements had antennabeamwidths of about 30° and about 15°, at 28 GHz and 73 GHzrespectively. FIG. 8 shows most or all the TX and RX locations where thepropagation measurements were conducted.

In (O. Kanhere, S. Ju, Y. Xing, and T. S. Rappaport, “Map AssistedMillimeter Wave Localization for Accurate Position Location,” in IEEEGlobal Communications Conference, Dec. 2019, pp. 1-6), the performanceof NYURay was evaluated by comparing the variation in the predicted andmeasured path loss with TR separation distance, as is illustrated inFIG. 7. For example, FIG. 7 shows an exemplary graph illustrating thecomparison between the measured and predicted powers of 22 TX-RX links(e.g., measures LOS 705, predicted LOS 710, measures NLOS 715 andpredicted NLOS 720) according to an exemplary embodiment of the presentdisclosure. The 22 TX-RX links that were chosen for the comparison werewidely spread. The received power was predicted to within 6.5 dB, exceptfor one outlier (e.g., link TX 4 RX 16) which had a prediction error of13.4 dB.

Exemplary Environmental Imaging

Radar at mmWave and THz frequencies can be more effective than light orinfrared-based imaging such as Light Detection and Ranging (“LIDAR”),due to the smaller impact that weather and ambient light can have on theTHz channel. While LIDAR can provide higher resolution, LIDAR cannotwork when it is foggy, raining, or cloudy. mmWave and THz radar can beused for assisting driving or flying in foul weather, as well as inmilitary and national security. High-definition video resolution radarsthat operate at several hundred gigahertz can be sufficient to provide aTV-like picture quality and can complement radars at lower frequencies(below 12.5 GHz) that provide longer range detection but with poorresolution. Dual-frequency radar systems can facilitate driving orflying in very heavy fog or rain. mmWave and THz waves can augment humanand computer vision to see around corners and to “view” NLOS objects,which can facilitate capabilities in rescue and surveillance, autonomousnavigation, and localization.

In prior work (M. Aladsani, A. Alkhateeb, and G. C. Trichopoulos,“Leveraging mmWave Imaging and Communications for SimultaneousLocalization and Mapping,” in International Conference on Acoustics,Speech, and Signal Processing (ICASSP), May 2019, pp. 1-4.), a 3-D imageof the environment was obtained via holographic 3-D imaging. A planeuniform linear array of antennas at the TX was used to calculate thesignal scattered from objects in the environment, R(x, y, f), measuredat point (x, y, z) using a vector network analyzer (“VNA”) at frequencyf, varying from about 220 to about 300 GHz. The image of the environmentf(x, y, z) was obtained as:

f(x,y,z)=IFT _(3-D) {FT _(2D) {R(x, y, f)}},   (2)

where IFT and FT can be the discrete spatial inverse Fourier transformand Fourier transform respectively.

A 3-D map of the environment may alternatively be generated for example,from point cloud that may be obtained from laser scanners. Anotherexample technique for 3-D map generation is to utilize the floorplan ofthe building to identify the location of walls, doors, windows and othermajor architectural elements on the floor, and to extrude thearchitectural elements to their known heights.

Exemplary Precise Position Location

mmWave imaging and communications can be incorporated in portabledevices operating above, for example, 100 GHz. By building ordownloading the map of the environment, an exemplary mobile device canbe used to predict the signal level, using real time site-specificprediction, or uploading of the map to the cloud that compiles physicalmaps, or which uses such maps for mobile applications. Based on thelarge bandwidth available at frequencies above 100 GHz, the LOS andNLOS, the exemplary system, method and computer-accessible medium canlocalize users with centimeter accuracy.

Additionally, the exemplary system, method and computer-accessiblemedium can utilize mmWave or THz imaging to reconstruct 3D maps of thesurroundings in unknown environments, thus merging sensing, and imagingand position location all at the same time. mmWave and THz signals canreflect strongly from most building materials which can facilitate theimaging of hidden objects (e.g., NLOS imaging). Additionally, scatteringcan also be well modeled and predicted. Based on the 3D maps of thephysical surroundings, and the time and angular information from amobile TOA and AOA, centimeter level localization and mapping can beachieved with the massive bandwidth and large antenna arrays at mmWaveand THz frequencies.

In a multipath rich environment, source rays can arrive at the RX via adirect path (e.g., if the direct path exists) as well as paths alongwhich the source rays can suffer multiple reflections. With knowledge ofthe angles at which the source rays arrive at the RX, the ToF of thesource rays and a 3-D map of the surrounding environment, the RX candetermine the location of the source.

In prior work (A. O. Kaya, L. Greenstein, D. Chizhik, R. Valenzuela, andN. Moayeri, “Emitter Localization and Visualization (ELVIS): A BackwardRay Tracing Algorithm for Locating Emitters,” in 2007 41st AnnualConference on Information Sciences and Systems, Mar. 2007, pp.376-381.), the rays along each AoA were backpropagated, as though the RXwere emitting the rays. Along the back-propagating path, the ray caninteract with obstructions in a manner identical to how aforward-propagating ray can interact. The intersection of two or moreback-traced rays were labeled as candidate TX locations. The TX can belocalized to the weighted sum of all candidate TX locations. Thecandidates that had several other intersections in their vicinity can begiven greater weight. Using six RX locations, assuming perfect AoAresolution, a 90 percentile localization error of 5 m was achieved in(A. O. Kaya, L. Greenstein, D. Chizhik, R. Valenzuela, and N. Moayeri,“Emitter Localization and Visualization (ELVIS): A Backward Ray TracingAlgorithm for Locating Emitters,” in 2007 41st Annual Conference onInformation Sciences and Systems, Mar. 2007, pp. 376-381.). The thermalnoise floor was about −85 dBm and an about 30 dBm, 300 MHz signal wastransmitted by the TX.

Exemplary Localization with One or More BSs

Map-assisted positioning with angle and time (“MAP-AT”) can be orinclude a map-assisted localization procedure. Localization can bepossible using a single BS, when at least two multipath componentsarrive at the user. The user does not need to be in LOS with the BS.MAP-AT can facilitate two types of BS-user configurations: the user caneither be the TX or the RX of a known radio signal. If the user can bethe TX, ToF and AoA information can be utilized. If the user can be theRX, ToF and AoD information can be utilized. Temporal and angularmeasurements can impose constraints on the possible locations of a user.A 3-D map of the indoor environment can create additional constraints onthe user's location. These exemplary constraints are explained below.

Exemplary Configuration I—User in Reception Mode

In configuration-I, the user receives a known signal from the BS. The BScan calculate the AoD of each multipath signal that reaches the userduring initial access. The ToF of each multipath component from the BSarriving at the user can either be estimated via the round trip time ofthe multipath component or the one-way propagation time. The BS can thensend the AoD and ToF of each multipath component to the user via afeedback channel.

For example, consider the case when there can be at most one reflectionor transmission of the signal before it reaches the BS. If the ToF andAoD of a multipath signal that reaches the BS can be known, there can betwo possible locations of the user. FIG. 9 shows an exemplary diagramillustrating two candidate locations of a user according to an exemplaryembodiment of the present disclosure. The two candidate locations of auser, CL₁ and CL₂, can correspond to the cases where the signal sentfrom the BS is reflected by and passes through the obstruction,respectively. Additionally, as shown in FIG. 9, if the ray reached theuser after one reflection, the user and BS must lie on the same side ofthe reflecting object. If the ray reached the BS directly from the user,or through one obstruction, the BS and user must lie on opposite sidesof the obstruction. The possible locations of the user, based on ToF andAoD at the BS can be referred to as candidate locations.

The exemplary procedure for locating candidate locations can be repeatedfor all multipath components. If a single multipath component can bereceived by the user, the BS may not be able to determine whichcandidate location corresponds to the user's true location.

However, when two or more multipath components arrive at the user, amajority of the candidate locations can correspond to the true locationof the user. For each multipath component arriving at the user, onecandidate location calculated based on the AoD and ToF of the multipathcomponent can correspond to the true user's location.

FIG. 10 is an exemplary diagram illustrating three multipath componentsthat arrive at the user according to an exemplary embodiment of thepresent disclosure, which includes all the candidate locations whenthree multipath components can be received by the user (“RX”) from theBS (“TX”). The user's location can correspond to the candidate locationidentified by the maximum number of multipath components. In particular,as shown in FIG. 10, three multipath components arrive at the user RX;one LOS component 1005 and two NLOS components 1010 and 1015. Of the sixcandidate locations for the user, based on AoD and ToF measurements atthe BS (e.g., CL₁-CL₆), three candidate locations (e.g., CL₃, CL₅, CL₆)correspond to the actual location of the user. The position of the useris estimated to be the modal candidate location (e.g., CL₃, CL₅, CL₆).

Errors in ToF measurements can cause the BS to incorrectly estimate thepath length to the user. Due to inaccurate AoD measurements, the BS mayincorrectly estimate the user's bearing. As a result, a candidatelocations estimated by MAP-AT using imprecise ToF and AoD informationmay not coincide with the user's true location. However, it can belikely that the candidate locations can be close to the user's truelocation, and thus close to one-another. MAP-AT can be modified to firstgroup the candidate locations that can be close to one another (e.g., ata distance of up to d=40 cm, where d can be a tunable parameter) to formcandidate location clusters. The user position can be estimated to bethe centroid of the candidate location group with maximal members.

MAP-AT can be generalized to use multiple BSs. Exemplary candidatelocations can be determined, corresponding to all the multipathcomponents received by the user from all BSs. MAP-AT can then proceedsin a similar fashion to the case when only one BS was utilized, byfinding the candidate location identified by the maximum number ofmultipath components.

Exemplary Configuration-II—User in the Transmission Mode

In configuration-II, the user transmits a known signal to the BS. The BScan calculate the AoA of each multipath signal that reach the BS. TheToF of the each multipath component from the user arriving at the BS caneither be estimated via the round trip time of the multipath componentor the one-way propagation time. If the ToF can be estimated viaround-trip time, the BS does not need to send the ToF of the multipathcomponents to the user via a feedback channel. Additionally, anexemplary synchronization between the user and the BS may not be needed.The BS may only need to send the AoA of each multipath component to theuser.

Candidate locations of the RX can be found analogously toconfiguration-I above. The user's location can correspond to thecandidate location identified by the maximum number of multipathcomponents.

Exemplary Simulations and Results

Simulations of localization using MAP-AT were conducted at 73 GHz bysynthesizing ToF and AoD measurements at 30 TR combinations via NYURay,of which 20 were in NLOS and 10 were in LOS. The BS and user locationschosen taken from the previous indoor propagation measurement campaignconducted at the NYU WIRELESS research center on the 9th floor of 2MetroTech Center in downtown Brooklyn, N.Y. (G. R. MacCartney, Jr. etal., “Indoor office wideband millimeter-wave propagation measurementsand models at 28 GHz and 73 GHz for ultra-dense 5G wireless networks(Invited Paper),” IEEE Access, vol. 3, pp. 2388-2424, Oct. 2015.)

The research center is a typical large office, with cubicles, walls madeof drywalls and windows. FIG. 8 shows an exemplary map illustrating afloor for testing the exemplary system, method and computer-accessiblemedium according to an exemplary embodiment of the present disclosure.Since localization accuracy may not depend on the user configuration(e.g., in configuration I or II), without loss of generality,configuration-I was chosen to analyze the performance of the exemplarypositioning procedure.

The position of each user was determined using a single BS. To make thesimulations realistic, zero mean Gaussian noise with standard deviationσ_(AoD)=0.5° was added to the AoD measurements. Three difference levelsof Gaussian noise were added to ToF measurements. The standard deviationσ_(ToF) was set to about 0.25 ns, about 0.5 ns and about 1 ns. Thepositioning error for each user was defined to be equal to the 3-DEuclidean distance between the position estimate and the true positionof the user. The rms positioning error for each user was calculated over100 simulations at all three ToF measurement noise levels.

TABLE I Performance of the map-assisted localization procedure fordifferent TR separation distances in LOS and NLOS environments. TX-RXNumber Mean Localization distance Env. of Users error (cm) <10 m LOS 94.1 NLOS 16 18.8 10-20 m LOS 1 9.4 NLOS 4 16.6 (all) LOS 10 4.6 NLOS 2018.3

TABLE II Performance of the map-assisted localization procedure withmore than one BS. BS-User Link Number Mean Localization Type of UsersError (cm) 1 LOS, 1 NLOS 9 4.6 2 NLOS 17 9.6 1 LOS, 2 NLOS 6 4.8 3 NLOS6 5.6Exemplary Localization Performance with One BS

With noise levels of σ_(AoD)=0.5° and σ_(ToF)=0.25 ns, the mean rmspositioning error was about 4.6 cm in LOS conditions and about 18.3 cmin NLOS conditions, over a total of 30 user locations. Table I aboveillustrates how the localization error varies with TR separationdistance in LOS and NLOS environments. Increasing σ_(ToF) to 1 nsdegraded the performance of the procedure. Despite the two outliers withrms localization errors of about 1.9 m and about 3.5 m, the medianlocalization accuracy was about 17.5 cm, with a mean localization errorof about 38.7 cm.

Exemplary Localization Performance with Multiple BSs

Of the 30 user locations previously considered, 26 locations were withinthe range of at least two BS. 12 user locations were within the range ofthree BSs. The performance of the map-assisted localization procedurewith more than one BS is summarized in Table II above. For the users notin LOS of any BS, increasing the number of BSs used for localization canfacilitate a reduction in the localization error. When one BS can beused to localize a NLOS user, an average localization error of about19.2 cm can be achieved. With two and three BSs, the localization errordropped to about 9.6 cm and about 5.6 cm respectively. The localizationerror for users in LOS remains constant (e.g., <5 cm).

The cumulative density functions (“CDFs”) of the positioning errors,with one, two, and three B Ss are shown in FIG. 11. In particular, FIG.11 shows an exemplary graph illustrating the cumulative densityfunctions of the positioning error when a user is localized using one BS(e.g., elements 1105, 1120 and 1135), two BSs (e.g., element 1110, 1125and 1140) and three BSs (e.g., elements 1115, 1130 and 1145), withσ_(AoD)=0.5° according to an exemplary embodiment of the presentdisclosure. For example,

When a single BS can be used, the rms errors were about 3.5 m and about1.9 m for two outlier user locations. However, when three BSs wereutilized, all users were localized to within about 25 cm.

The exemplary system, method and computer-accessible medium, accordingto an exemplary embodiment of the present disclosure, can include MAP-ATfor map-assisted data fusion of AoD and ToF information, to providecentimeter-level localization. A 3-D map of the environment (generatedon-the-fly by the user or downloaded a-priori), in concert with AoD andToF information can be used to predict the propagation path of multipathcomponents received by the user. MAP-AT was tested using NYURay, a 3-Dray tracer developed herein. The ray tracer was calibrated based onreal-world measurements conducted at 28 and 73 GHz, and predicted thereceived power accurately to within 6.5 dB. Based on simulationsconducted at 30 RX locations corresponding to the real world measurementlocations, a mean localization accuracy of 4.6 cm in LOS and 18.8 cm inNLOS was achieved in a typical large office environment using a singleBS per user, with TR separation distances varying from 3 m to 14.5 m. Byusing three BSs, the localization error for LOS users remained the samewhile the localization error for NLOS users dropped to 5.6 cm.

Exemplary Position location in 3GPP

Exemplary support for UE position location in the 3GPP standard can betraced back to the E-911 location service regulations set forth by theFCC in 1996 (See T. S. Rappaport, J. H. Reed, and B. D. Woerner,“Position location using wireless communications on highways of thefuture,” IEEE Communications Magazine, vol. 34, no. 10, pp. 33-41, Oct.1996.) (See J. H. Reed, K. J. Krizman, B. D. Woerner, and T. S.Rappaport, “An overview of the challenges and progress in meeting theE-911 requirement for location service,” IEEE Commun. Mag., vol. 36, no.4, pp. 30-37, 1998.). Today, with the ubiquitous nature of cell phones,80% E-911 calls are placed from wireless devices and hence wirelessposition location is even more important. Widespread adoption of aposition location algorithm by network operators and cell phonemanufacturers can be made possible if the algorithm isstandard-compliant. To develop standard-compliant position locationalgorithms, one may need to understand 3GPP position location, thewireless channel parameters currently used for 3GPP position location,as well as potential channel features that may be introduced in future3GPP releases.

Exemplary Position Location Techniques Supported by 3GPP

An exemplary 3GPP position location technique, Cell ID (CID) proximitypositioning, can estimate the UE to be at the serving BS. With EnhancedCID (E-CID), CID accuracy can be improved by incorporating referencesignal strength (RSS) measurements at the UE, and timing advance (TA)and AoA measurements at the BS. Due to the varying distances of UEs fromthe BS, the uplink data can arrive at the BS with a delay proportionateto the BS-UE distance. To ensure uplink frame time-alignment, the TA canbe sent as downlink feedback and can facilitate UEs to adjust theiruplink transmission, and thus, can provide an estimate of the round triptime (RTT) of the first arriving MPC at the BS, as shown in, e.g., FIG.14. FIG. 14 shows an exemplary TA (δ₁) that can ensure uplink frames ofall UEs are aligned. The time of flight (ToF) of the first arriving MPC(the one-way travel time) can be equal to half the RTT. The minimumreportable one-way distance d_(min), calculated from the ToF, candecrease with an increase in subcarrier spacing (SCS) and can be givenby

d_(min)b=78.12/2 ^(μ),   (1)

when the SCS is 2^(μ)×15 kHz. In 5G-NR, the maximum SCS is 60 kHz forlower frequency bands (below 6 GHz), corresponding to a minimumreportable distance of 19.52 m, while the maximum SCS is 480 kHz forhigher frequency bands (above 24 GHz), corresponding to a minimumreportable distance of 2.44 m.

Although specific details of how to utilize the RSS and TA measurementsare not provided in the standard, TA and/or RSS can be utilized toestimate the distance between the BS and UE. The distance estimate canbe combined with AoA to calculate the position of the UE via simplegeometric calculations.

While antenna arrays at BS are becoming more prevalent due to the needfor beamforming at mmWave frequencies, some current BSs may not befitted with antenna arrays due to cost considerations. In the absence ofAoA, multiple distance estimates (estimated from RSS/TA) can also beused for UE position location (via trilateration) if the UE is in thecoverage area of at least three BSs. The primary BS may initiate forcedhandovers, allowing the UE to estimate the TA/RSS from other neighboringBSs.

GNSS receivers (RXs), present in nearly many modern cellular devices,can localize a UE to within 5 m when four or more satellites aredirectly visible However, in urban canyons where the direct path tosatellites is blocked, GNSS performance may deteriorate. With assistedGNSS (A-GNSS), 3GPP networks can improve GNSS performance by providingassistance information to the UE that can improve RX sensitivity, andreduce time to first fix (TTFF) and UE power consumption. The cellularnetwork can provide external information that improves the GNNS positionTTFF by utilizing a coarse estimate of the UE location (for instance viaE-CID) to reduce the frequency/code-delay search space. The lower TTFFallows UEs to consume less power as the GNSS RX need not be always-on.

Downlink TDoA (e.g., called observed time difference of arrival (O-TDOA)in 3GPP can be measured at UE. The difference in time at which the PRSis received at the UE from two BSs is called the reference signal timingdifference (RSTD). Since the BSs can be synchronized via GNSSsatellites, RSTD may have a direct relation to the geometric differencein distance of the two BSs from the UE. O-TDoA supports a timeresolution of 0.5 T_(s) (4.88 m), when RSTD≤4096 T_(s) and a timeresolution of 1 T_(s) (9.67 m) when 4096 T_(s)≤RSTD≤15391 T_(s), where 1T_(s)=32.522 ns (9.76 m).

Just as O-TDoA is measured at the UE, uplink time difference of arrival(UTDOA) can be measured at the BS, allowing UEs lacking capabilities tomake OTDoA measurements to be localized. The SRS, a Zadoff-Chu sequencetransmitted by the UE, is utilized by two or more pairs of BS to measurethe relative time of arrival. A minimum resolution of 2 T_(s) (19.51 m)is possible.

To improve the vertical component of UE position location, thebarometric positioning method can be used, whereas the atmosphericpressure at the UE can be measured using barometric pressure sensorsfound in, e.g., most modern cell phones. Since the atmospheric pressuredecreases with an increase in UE height, by calibrating the barometricpressure sensor to the atmospheric pressure at a known height, thevertical UE position can be determined.

With the advent of ultra-wide bandwidths due to the utilization ofmmWave frequency bands, MPCs can now be resolved to a finer timeresolution. Localization techniques that exploit multipath informationmay require delay and angle measurements of more than one path.Currently, 3GPP supports the reporting of the relative delay of all MPCswith respect to the MPC utilized to calculate the RSTD, via theadditional path information element with a resolution of 0.5 T_(s) (4.88m). By adding the relative MPC delay to TA, the absolute time of arrivalof individual MPCs can be calculated. 3GPP currently supports themeasurement of the AoA of only one signal at the BS, due to which AoAinformation of individual MPCs is lacking.

FIGS. 12A and 12B show an exemplary flow diagram of a method 2000according to an exemplary embodiment of the present disclosure. Forexample, at procedure 2005, a direction of transmission of an adaptiveantenna array can be modified based on an environment. At procedure2010, a first millimeter wave (mmWave) radiofrequency (RF) radiation canbe generated using a mobile device. At procedure 2015, the first mmWaveRF radiation can be provided to the environment. At procedure 2020, themobile device can receive the second mmWave RF radiation from theenvironment that can be based on the first mmWave RF radiation. Atprocedure 2025, information regarding the second mmWave RF radiation canbe transmitted to a further device, which can be used to generate animage or video.

Additionally, at procedure 2030, information related to a phase, a timeof arrival, a relative time of arrival, or an angle of arrival of thesecond mmWave RF radiation can be determined. At procedure 2035, adistance between the mobile device and a receiving device can bedetermined based on the phase. At procedure 2040, a phase ambiguity inthe phase of the second mmWave RF radiation can be corrected for. Atprocedure 2045, a scattering pattern of different angles of receipt ofthe second mmWave RF radiation can be determined by a receiving device.At procedure 2050, an image can be generated, a video can be generated,a map can be generated, movement of an object can be tracked, a presenceof location of an object can be determined, and/or a location of a studof a wall can be determined.

The performance of MAP-AT was considered at the RX locations wheremmWave measurements were conducted at 28, 73 and 140 GHz. FIG. 15 showsan exemplary illustration of an exemplary RMS positioning error of themethod of present disclosure according to an exemplary embodiment. Threeconcentric error circles of radii equal to the RMS positioning errorcorresponding to the three levels of noise added to ToF measurementshave been drawn centered at each user location. The error circles fortwo outlier user locations having errors of 4.0 m and 4.9 m have notbeen plotted. The localization error of the outliers can be reduced byusing additional BSs for positioning the users.

Cell phones in the future shall likely have the capability to generate amap of the environment on the fly and “see in the dark” without adedicated ranging RF front end. The UE could behave like a radar,measuring the distances of prominent features in the environment, suchas walls, doors, and other obstructions. FIG. 16 is an exemplary mapgenerated on the-the-fly using a mmWave radar on a cell phone.

FIG. 17 shows a block diagram of an exemplary embodiment of a systemaccording to the present disclosure. For example, exemplary proceduresin accordance with the present disclosure described herein can beperformed by a processing arrangement and/or a computing arrangement2305. Such processing/computing arrangement 2305 can be, for exampleentirely or a part of, or include, but not limited to, acomputer/processor 2310 that can include, for example one or moremicroprocessors, and use instructions stored on a computer-accessiblemedium (e.g., RAM, ROM, hard drive, or other storage device).

As shown in FIG. 17, for example a computer-accessible medium 2315(e.g., as described herein above, a storage device such as a hard disk,floppy disk, memory stick, CD-ROM, RAM, ROM, etc., or a collectionthereof) can be provided (e.g., in communication with the processingarrangement 2305). The computer-accessible medium 2315 can containexecutable instructions 2320 thereon. In addition or alternatively, astorage arrangement 2325 can be provided separately from thecomputer-accessible medium 2315, which can provide the instructions tothe processing arrangement 2305 so as to configure the processingarrangement to execute certain exemplary procedures, processes, andmethods, as described herein above, for example.

Further, the exemplary processing arrangement 2305 can be provided withor include an input/output ports 2335, which can include, for example awired network, a wireless network, the internet, an intranet, a datacollection probe, a sensor, etc. As shown in FIG. 17, the exemplaryprocessing arrangement 2305 can be in communication with an exemplarydisplay arrangement 2330, which, according to certain exemplaryembodiments of the present disclosure, can be a touch-screen configuredfor inputting information to the processing arrangement in addition tooutputting information from the processing arrangement, for example.Further, the exemplary display arrangement 2330 and/or a storagearrangement 2325 can be used to display and/or store data in auser-accessible format and/or user-readable format.

The foregoing merely illustrates the principles of the disclosure.Various modifications and alterations to the described embodiments willbe apparent to those skilled in the art in view of the teachings herein.It will thus be appreciated that those skilled in the art will be ableto devise numerous systems, arrangements, and procedures which, althoughnot explicitly shown or described herein, embody the principles of thedisclosure and can be thus within the spirit and scope of thedisclosure. Various different exemplary embodiments can be used togetherwith one another, as well as interchangeably therewith, as should beunderstood by those having ordinary skill in the art. In addition,certain terms used in the present disclosure, including thespecification, drawings and claims thereof, can be used synonymously incertain instances, including, but not limited to, for example, data andinformation. It should be understood that, while these words, and/orother words that can be synonymous to one another, can be usedsynonymously herein, that there can be instances when such words can beintended to not be used synonymously. Further, to the extent that theprior art knowledge has not been explicitly incorporated by referenceherein above, it is explicitly incorporated herein in its entirety. Allpublications referenced are incorporated herein by reference in theirentireties.

Additionally, all systems, methods, computer-accessible mediums andapparatuses described herein can incorporate, use, operate, etc. any ofthe subject matter described in U.S. Provisional Patent Application No.62/852,053, filed on May 23, 2019, which has been incorporated herein byreference in its entirety.

EXEMPLARY REFERENCES

The following references are hereby incorporated by reference, in theirentireties:

-   [1] https://ecfsapi.fcc.gov/file/0228024926034/FCC-18-17A6.pdf.-   [2] https://www.fcc.gov/ecfs/filing/0228024926034.-   [3] https://ecfsapi.fcc.gov/file/0228024926034/FCC-18-17A1.pdf.-   [4] Rappaport, et. al., “Millimeter Wave for 5G Cellular: It will    work,” IEEE Access 2013.-   [5] S. Sun, et. al., “MIMO for millimeter-wave wireless    communications: Beamforming, spatial multiplexing, or both?,” IEEE    Com. Mag., 2014.-   [6] S. Sun, et. al, “Hybrid Beamforming for 5G Millimeter-Wave    Multi-Cell Networks,” IEEE InfoCom Honolulu, April 2018.-   [7] C. M. P. Ho, et. al, “Antenna effects on indoor obstructed    wireless channels and a deterministic image-based wide-band    propagation model for in-building personal communication systems,”    Intl. Journal of Wireless Information Networks, January 1994, pp    61-76,1994.-   [8] T. S. Rappaport and D. A. Hawbaker, Wide-band microwave    propagation parameters using circular and linear polarized antennas    for indoor wireless channels, IEEE Transactions on Communications,    Vol. 40, No. 2, Feb. 1992.-   [9] T. S. Rappaport, “Wireless Communications, Principles and    Practice” c. 2002 Pearson/Prentice Hall.-   [10] Millimeter Wave Mobile Communications for 5G Cellular: It will    work!” by Rappaport, et. al.-   [11] O. Kanhere and T. S. Rappaport, “Position Locationing for    Millimeter Wave Systems,” in Proc. IEEE 2018 Global Communications    Conference, Dec. 2018, pp. 1-6.-   [12] G. R. MacCartney, Jr. and T. S. Rappaport, “A flexible    millimeter-wave channel sounder with absolute timing,” IEEE Journal    on Selected Areas in Communications, vol. 35, no. 6, pp. 1402-1418,    June 2017.-   [13] T. S. Rappaport et al., “Wireless Communications and    Applications Above 100 GHz: Opportunities and Challenges for 6G and    Beyond (Invited),” in IEEE Access, vol. 7, pp 78729-28757, May 2019.-   [14] J. S. Parker, P. Mickelson, J. Yeak, K. Kremeyer, and J. Rife,    “Exploiting the Terahertz Band for Radionavigation,” Journal of    Infrared, Millimeter, and Terahertz Waves, vol. 37, no. 10, pp.    1021-1042, Oct. 2016.-   [15] T. S. Rappaport, Wireless Communications: Principles and    Practice, 2nd ed. Upper Saddle River, N.J.: Prentice Hall, 2002.-   [16] C. D. McGillem and T. S. Rappaport, “Infra-red location system    for navigation of autonomous vehicles,” in Proceedings. 1988 IEEE    International Conference on Robotics and Automation, vol. 2, Apr.    1988, pp. 1236-1238.-   [17]—, “A beacon navigation method for autonomous vehicles,” IEEE    Transactions on Vehicular Technology, vol. 38, no. 3, pp. 132-139,    Aug. 1989.-   [18] J. I. Bowditch, American practical navigator. Washington, D.C.:    US Government Printing Office, 1938.-   [19] T. Chi, M. Huang, S. Li, and H. Wang, “A packaged 90-to-300 GHz    transmitter and 115-to-325 GHz coherent receiver in CMOS for    full-band continuous-wave mm-wave hyperspectral imaging,” in 2017    IEEE International Solid-State Circuits Conference (ISSCC), Feb    2017, pp. 304-305.-   [20] S. Kiran Doddalla and G. C. Trichopoulos, “Non-line of sight    terahert imaging from a single viewpoint,” in 2018 IEEE/MTT-S    International Microwave Symposium-IMS, June 2018, pp. 1527-1529.-   [21] G. R. MacCartney, Jr. et al., “Indoor office wideband    millimeter-wave propagation measurements and models at 28 GHz and 73    GHz for ultra-dense 5G wireless networks (Invited Paper),” IEEE    Access, vol. 3, pp. 2388-2424, Oct. 2015.-   [22] K. R. Schaubach, N. J. Davis, and T. S. Rappaport, “A ray    tracing method for predicting path loss and delay spread in    microcellular environments,” in Vehicular Technology Society 42nd    VTS Conference—Frontiers of Technology, vol. 2, May 1992, pp.    932-935.-   [23] G. Durgin, N. Patwari, and T. S. Rappaport, “An advanced 3D ray    launching method for wireless propagation prediction,” in 1997 IEEE    47th Vehicular Technology Conference. Technology in Motion, vol. 2,    May 1997, pp. 785-789.-   [24] J. W. McKown and R. L. Hamilton, “Ray tracing as a design tool    for radio networks,” IEEE Network, vol. 5, no. 6, pp. 27-30, Nov.    1991.-   [25] C. M. Peter Ho, T. S. Rappaport, and M. P. Koushik, “Antenna    effects on indoor obstructed wireless channels and a deterministic    image-based wide-band propagation model for in-building personal    communication systems,” International Journal of Wireless    Information Networks, vol. 1, no. 1, pp. 61-76, Jan. 1994.-   [26] S. Y. Tan and H. S. Tan, “A microcellular communications    propagation model based on the uniform theory of diffraction and    multiple image theory,” IEEE Transactions on Antennas and    Propagation, vol. 44, no. 10, pp. 1317-1326, Oct. 1996.-   [27] Y. Xing, O. Kanhere, S. Ju, and T. S. Rappaport, “Indoor    Wireless Channel Properties at Millimeter Wave and Sub-Terahertz    Frequencies: Reflection, Scattering, and Path Loss,” in IEEE 2019    Global Communications Conference, pp. 1-6, Dec. 2019.-   [28] S. Ju et al., “Scattering Mechanisms and Modeling for Terahertz    Wireless Communications,” in 2019 IEEE International Conference on    Communications (ICC), May 2019, pp. 1-7.-   [29] S. Sun, G. R. MacCartney, Jr., and T. S. Rappaport,    “Millimeter-wave distance-dependent large-scale propagation    measurements and path loss models for outdoor and indoor 5G    systems,” in Proc. 10th EuCap, Davos, Switzerland, Apr. 2016, pp.    1-5.-   [30] T. S. Rappaport et al., “Millimeter Wave Mobile Communications    for 5G Cellular: It Will Work!” IEEE Access, vol. 1, pp. 335-349,    May 2013.-   [31] A. O. Kaya, L. Greenstein, D. Chizhik, R. Valenzuela, and N.    Moayeri, “Emitter Localization and Visualization (ELVIS): A Backward    Ray Tracing Algorithm for Locating Emitters,” in 2007 41st Annual    Conference on Information Sciences and Systems, Mar. 2007, pp.    376-381.-   [32] M. Aladsani, A. Alkhateeb, and G. C. Trichopoulos, “Leveraging    mmWave Imaging and Communications for Simultaneous Localization and    Mapping,” in International Conference on Acoustics, Speech, and    Signal Processing (ICASSP), May 2019, pp. 1-4.-   [33] P. Meissner, E. Leitinger, M. Frohle, and K. Witrisal,    “Accurate and Robust Indoor Localization Systems Using    Ultra-wideband Signals,” in European Navigation Conference (ENC),    Apr. 2013, pp. 1-9.-   [34] B5GS19, “The Brooklyn 5G Summit,” Apr. 2019. [Online].    Available: https://brooklyn5gsummit.com/.-   [35] FCC, “Spectrum horizons,” First Report and Order ET Docket    18-21, Washington D. C., March 21,2019.-   [36] T. Nagatsuma, “Breakthroughs in photonics 2013: Thz    communications based on photonics,” IEEE Photonics Journal, vol. 6,    no. 2, pp. 1-5, April 2014.-   [37] Ministry of Internal Affairs and Communications, “Frequency    Assignment Plan (as of March 2019),” March 2019. [Online].    Available:    https://www.tele.soumu.go.jp/e/adm/freq/search/share/plan.htm-   [38] “Ieee standard for high data rate wireless multi-media    networks—amendment 2: 100 gb/s wireless switched point-to-point    physical layer,” IEEE Std 802.15.3d-2017 (Amendment to IEEE Std    802.15.3-2016 as amended by IEEE Std 802.15.3e-2017), pp. 1-55, Oct.    2017.-   [39] V. Petrov, D. Moltchanov, and Y. Koucheryavy, “Applicability    assessment of terahertz information showers for next-generation    wireless networks,” in 2016 IEEE International Conference on    Communications (ICC), May 2016, pp. 1-7.-   [40] V. Petrov, A. Pyattaev, D. Moltchanov, and Y. Koucheryavy,    “Terahertz band communications: Applications, research challenges,    and standardization activities,” in 2016 8th International Congress    on Ultra Modern Telecommunications and Control Systems and Workshops    (ICUMT), Oct. 2016, pp. 183-190.-   [41] mmWave Coalition, “mmWave Coalition's NTIA comments,”    Jan. 2019. [Online]. Available:    http://mmwavecoalition.org/mmwave-coalition-millimeter-waves/mmwave-coalitions-ntia-comments/-   [42] K. Sengupta, T. Nagatsuma, and D. M. Mittleman, “Terahertz    integrated electronic and hybrid electronic-photonic systems,”    Nature Electronics, vol. 1, no. 12, p. 622,2018.-   [43] Federal Communications Commission, “Use of Spectrum Bands Above    24 GHz For Mobile Radio Services: GN Docket No. 14-177,” Dec 2018.    [Online]. Available:    https://docsfcc.gov/public/attachments/FCC-18-180A1.pdf-   [44] G. R. MacCartney, Jr. et al., “Millimeter wave wireless    communications: New results for rural connectivity,” in Proceedings    of the 5th Workshop on All Things Cellular: Operations, Applications    and Challenges: in conjunction with MobiCom 2016, ser. ATC '16. New    York, N.Y., USA: ACM, Oct. 2016, pp. 31-36.-   [45] G. R. MacCartney and T. S. Rappaport, “Rural macrocell path    loss models for millimeter wave wireless communications,” IEEE    Journal on Selected Areas in Communications, vol. 35, no. 7, pp.    1663-1677, July 2017.-   [146] T. S. Rappaport, J. N. Murdock, and F. Gutierrez, “State of    the art in 60-GHz integrated circuits and systems for wireless    communications,” Proceedings of the IEEE, vol. 99, no. 8, pp.    1390-1436, Aug. 2011.-   [47] T. S. Rappaport et al., “Millimeter Wave Mobile Communications    for 5G Cellular: It Will Work!” IEEE Access, vol. 1, pp. 335-349,    May 2013.-   [48]—, “Wireless Communications and Applications Above 100 GHz:    Opportunities and Challenges for 6G and Beyond (Invited),” IEEE    Access, Feb. 2019.-   [49] S. Priebe et al., “Channel and Propagation Measurements at 300    GHz,” IEEE Transactions on Antennas and Propagation, vol. 59, no. 5,    pp. 1688-1698, May 2011.-   [50] T. Kleine-Ostmann et al., “Measurement of channel and    propagation properties at 300 GHz,” in 2012 Conference on Precision    electromagnetic Measurements, July 2012, pp. 258-259.-   [51] N. Khalid and O. B. Akan, “Wideband THz communication channel    measurements for 5G indoor wireless networks,” in 2016 IEEE    International Conference on Communications (ICC), May 2016, pp. 1-6.-   [52] S. Sun, T. S. Rappaport, and M. Shafi, “Hybrid beamforming for    5g millimeter-wave multi-cell networks,” IEEE Conference on Computer    Communications Workshops (INFOCOM WKSHPS), Apr. 2018.-   [53] S. Ju. et al., “Scattering mechanisms and modeling for    terahertz wireless communications,” in in proceeding of 2018 IEEE    International Conference on Communications (ICC), 2018.-   [54] J. Ma, R. Shrestha, L. Moeller, and D. M. Mittleman, “Channel    performance for indoor and outdoor terahertz wireless links,” APL    Photonics, vol. 3, no. 5, pp. 1-13, Feb. 2018.-   [55] Y. Xing and T. S. Rappaport, “Propagation Measurement System    and Approach at 140 GHzMoving to 6G and Above 100 GHz,” in IEEE 2018    Global Communications Conference, Dec. 2018, pp. 1-6.-   [56] G. R. MacCartney and T. S. Rappaport, “A flexible    millimeter-wave channel sounder with absolute timing,” IEEE Journal    on Selected Areas in Communications, vol. 35, no. 6, pp. 1402-1418,    June 2017.-   [57] Y. Xing et al., “Verification and calibration of antenna    cross-polarization discrimination and penetration loss for    millimeter wave communications,” in 2018 IEEE 88th Vehicular    Technology Conference, Aug. 2018, pp. 1-6.-   [58] H. T. Friis, “A note on a simple transmission formula,”    Proceedings of the IRE, vol. 34, no. 5, pp. 254-256, May 1946.-   [59] R. Davies, M. Bensebti, M. A. Beach, and J. P. McGeehan,    “Wireless propagation measurements in indoor multipath environments    at 1.7 GHz and 60 GHz for small cell systems,” in [1991 Proceedings]    41st IEEE Vehicular Technology Conference, May 1991, pp. 589-593.-   [60] C. Thajudeen, A. Hoorfar, F. Ahmad, and T. Dogaru, “Measured    complex permittivity of walls with different hydration levels and    the effect on power estimation of twri target returns,” Progress in    Electromagnetics Research, vol. 30, pp. 177-199, 2011.-   [61] T. S. Rappaport, Wireless Communications: Principles and    Practice, 2nd ed. Upper Saddle River, N.J.: Prentice Hall, 2002.-   [62] V. Degli-Esposti, F. Fuschini, E. M. Vitucci, and G.    Falciasecca, “Measurement and modelling of scattering from    buildings,” IEEE Trans. on Ant. and Prop., vol. 55, no. 1, pp.    143-153, Jan 2007.-   [63] C. R. Anderson and T. S. Rappaport, “In-building wideband    partition loss measurements at 2.5 and 60 GHz,” IEEE Transactions on    Wireless Communications, vol. 3, no. 3, pp. 922-928, May 2004.-   [64] G. D. Durgin, T. S. Rappaport, and H. Xu, “Partition-based path    loss analysis for in-home and residential areas at 5.85 GHz,” in    1998 IEEE Global Communications Conference (GLOBECOM), vol. 2, Nov.    1998, pp. 904-909.-   [65] O. Kanhere and T. S. Rappaport, “Position locationing for    millimeter wave systems,” in IEEE 2018 Global Communications    Conference, Dec. 2018, pp. 1-6.-   [663] O. Kanhere, S. Ju, Y. Xing, and T. S. Rappaport, “Map Assisted    Millimeter Wave Localization,” in IEEE 2019 Global Communications    Conference, pp. 1-6, Dec. 2019.-   [67] G. D. Durgin, T. S. Rappaport, and H. Xu, “Measurements and    models for radio path loss and penetration loss in and around homes    and trees at 5.85 GHz,” IEEE Transactions on Communications, vol.    46, no. 11, pp. 1484-1496, Nov. 1998.-   [68] J. Ryan, G. R. MacCartney, Jr., and T. S. Rappaport, “Indoor    Office Wideband Penetration Loss Measurements at 73 GHz,” in IEEE    International Conference on Communications Workshop, May 2017, pp.    1-6.-   [69] B. Kapilevich et al., “Millimeter waves sensing behind    walls-feseability study with fel radiation,” 2007.-   [70] Y. P. Zhang and Y. Hwang, “Measurements of the characteristics    of indoor penetration loss,” in 1994 IEEE 44th Vehicular Technology    Conference (VTC), vol. 3, June 1994, pp. 1741-1744.-   [71] L. M. Frazier, “Radar surveillance through solid materials,” in    Command, Control, Communications, and Intelligence Systems for Law    Enforcement, vol. 2938. International Society for Optics and    Photonics, 1997, pp. 139-147.-   [72] A. K. M. Isa, A. Nix, and G. Hilton, “Impact of diffraction and    attenuation for material characterization in millimeter wave bands,”    in 2015 Loughborough Antennas Propagation Conference (LAPC), Nov    2015, pp. 1-4.-   [73] J. Kokkoniemi, J. Lehtomki, and M. Juntti, “Measurements on    penetration loss in terahertz band,” in 2016 10th European    Conference on Antennas and Propagation (EuCAP), Apr. 2016, pp. 1-5.-   [74] G. R. MacCartney, Jr. et al., “Indoor office wideband    millimeter-wave propagation measurements and models at 28 GHz and 73    GHz for ultra-dense 5G wireless networks (Invited Paper),” IEEE    Access, vol. 3, pp. 2388-2424, Oct. 2015.-   [75] S. Deng, M. K. Samimi, and T. S. Rappaport, “28 GHz and 73 GHz    millimeter-wave indoor propagation measurements and path loss    models,” in IEEE International Conference on Communications    Workshops (ICCW), June 2015, pp. 1244-1250.-   [76] S. Sun, G. R. MacCartney, Jr., and T. S. Rappaport,    “Millimeter-wave distance-dependent large-scale propagation    measurements and path loss models for outdoor and indoor 5G    systems,” in 2016 IEEE 10th European Conference on Antennas and    Propagation (EuCAP), Apr. 2016, pp. 1-5.-   [77] S. Sun et al., “Synthesizing omnidirectional antenna patterns,    received power and path loss from directional antennas for 5G    millimeter-wave communications,” in 2015 IEEE Global Communications    Conference (GLOBECOM), Dec. 2015, pp. 3948-3953.-   [78] I. F. Akyildiz, J. M. Jornet, and C. Han, “Terahertz band: Next    frontier for wireless communications,” Physical Communication, vol.    12, pp. 16-32, Sept. 2014.-   [79] M. J. W. Rodwell, Y. Fang, J. Rode, J. Wu, B. Markman, S. T.    uran Brunelli, J. Klamkin, and M. Urteaga, “100-340 GHz Systems:    Transistors and Applications,” in 2018 IEEE International Electron    Devices Meeting (IEDM), Dec. 2018, pp. 14.3.1-14.3.4.-   [80] J. Harvey et al., “Exploiting High Millimeter Wave Bands for    Military Communications, Applications, and Design,” IEEE Access,    vol. 7, pp. 52 350-52 359, Apr. 2019.-   [81] D. M. Mittleman, “Twenty years of terahertz imaging,” Opt.    Express, vol. 26, no. 8, pp. 9417-9431, Apr. 2018.-   [82] M. Aladsani, A. Alkhateeb, and G. C. Trichopoulos, “Leveraging    mmWave Imaging and Communications for Simultaneous Localization and    Mapping,” in International Conference on Acoustics, Speech, and    Signal Processing (ICASSP), May 2019, pp. 1-4.-   [83] T. S. Rappaport, “6G and Beyond: Terahertz Communications and    Sensing,” 2019 Brooklyn 5G Summit Keynote, Apr. 2019. [Online].    Available:    https://ieeetv.ieee.org/conference-highlights/ted-tours-brooklyn-5g-summit-2019?-   [84] O. Kanhere and T. S. Rappaport, “Position locationing for    millimeter wave systems,” in IEEE 2018 Global Communications    Conference, Dec. 2018, pp. 1-6.-   [85] H. Wang and T. S. Rappaport, “A parametric formulation of the    UTD diffraction coefficient for real-time propagation prediction    modeling,” IEEE Antennas and Wireless Propagation Letters, vol. 4,    pp. 253-257, Aug. 2005.-   [86] O. Kanhere, S. Ju, Y. Xing, and T. S. Rappaport, “Map Assisted    Millimeter Wave Localization for Accurate Position Location,” in    IEEE Globecom, Dec. 2019, pp. 1-6.-   [87] M. J. W. Rodwell, “Sub-mm-wave technologies: Systems, ICs, THz    transistors,” in 2013 Asia-Pacific Microwave Conference Proceedings    (APMC), Nov. 2013, pp. 509-511.-   [88] S. Kiran Doddalla and G. C. Trichopoulos, “Non-Line of Sight    Terahertz imaging from a Single Viewpoint,” in 2018 IEEE/MTT-S    International Microwave Symposium-IMS, June 2018, pp. 1527-1529.-   [89] S. Ju et al., “Scattering Mechanisms and Modeling for Terahertz    Wireless Communications,” in Proc. IEEE International Conference on    Communications, May 2019, pp. 1-7.-   [90] A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G.    Bawendi, and R. Raskar, “Recovering three-dimensional shape around a    corner using ultrafast time-of-flight imaging,” Nature    communications, vol. 3, p. 745, Mar. 2012.-   [91] M. O'Toole, D. B. Lindell, and G. Wetzstein, “Confocal    non-line-of-sight imaging based on the light-cone transform,”    Nature, vol. 555, no. 7696, p. 338, Mar. 2018.-   [92] F. Xu, G. Shulkind, C. Thrampoulidis, J. H. Shapiro, A.    Torralba, F. N. Wong, and G. W. Wornell, “Revealing hidden scenes by    photon-efficient occlusion-based opportunistic active imaging,”    Optics express, vol. 26, no. 8, pp. 9945-9962, Apr. 2018.-   [93] A. Sume, M. Gustafsson, M. Herberthson, A. Janis, S.    Nilsson, J. Rahm, and A. Orbom, “Radar detection of moving targets    behind corners,” IEEE Transactions on Geoscience and Remote Sensing,    vol. 49, no. 6, pp. 2259-2267, June 2011.-   [94] K. Thai, O. Rabaste, J. Bosse, D. Poullin, I. Hinostroza, T.    Letertre, and T. Chonavel, “Around-the-corner radar: Detection and    localization of a target in non-line of sight,” in 2017 IEEE Radar    Conference (RadarConf), May 2017, pp. 0842-0847.-   [95] P. Setlur, T. Negishi, N. Devroye, and D. Erricolo, “Multipath    exploitation in non-los urban synthetic aperture radar,” IEEE    Journal of Selected Topics in Signal Processing, vol. 8, no. 1, pp.    137-152, Feb. 2014.-   [96] R. Zetik, M. Eschrich, S. Jovanoska, and R. S. Thoma, “Looking    behind a corner using multipath-exploiting uwb radar,” IEEE    Transactions on Aerospace and Electronic Systems, vol. 51, no. 3,    pp. 1916-1926, July 2015.-   [97] T. S. Rappaport et al., “Small-scale, local area, and    transitional millimeter wave propagation for 5G communications,”    IEEE Transactions on Antennas and Propagation, vol. 65, no. 12, pp.    6474-6490, Dec. 2017.-   [98] G. C. Trichopoulos, H. L. Mosbacker, D. Burdette, and K.    Sertel, “A Broadband Focal Plane Array Camera for Real-time THz    Imaging Applications,” IEEE Transactions on Antennas and    Propagation, vol. 61, no. 4, pp. 1733-1740, Apr. 2013.-   [99] T. Chi, M. Huang, S. Li, and H. Wang, “ A packaged 90-to-300    GHz transmitter and 115-to-325 GHz coherent receiver in CMOS for    full-band continuous-wave mm-wave hyperspectral imaging,” in 2017    IEEE International Solid-State Circuits Conference (ISSCC), Feb    2017, pp. 304-305.-   [100] J. Zhou et al., “Integrated Full Duplex Radios,” IEEE    Communications Magazine, vol. 55, no. 4, Apr. 2017, pp. 142-51.-   [101] C. A. Balanis, Antenna Theory: Analysis and Design, 4th ed.,    Wiley, 2016-   [102] T. S. Rappaport, J. H. Reed, and B. D. Woerner, “Position    location using wireless communications on highways of the future,”    IEEE Communications Magazine, vol. 34, no. 10, pp. 33-41, Oct. 1996.-   [103] J. H. Reed, K. J. Krizman, B. D. Woerner, and T. S. Rappaport,    “An overview of the challenges and progress in meeting the E-911    requirement for location service,” IEEE Commun. Mag., vol. 36, no.    4, pp. 30-37, 1998.-   [103] FCC, “Wireless E911 Location Accuracy Requirements,” Fifth    Report and Order PS Docket 07-114., Apr. 2019.-   [104] F. Van Diggelen and P. Enge, “The worlds first GPS MOOC and    worldwide laboratory using smartphones,” in Proc. 28th Int. Tech.    meeting Satellite Divis. Inst. Navigat. (ION GNSS+ 2015), Sept.    2015, pp. 361-369.-   [105] 3GPP, “UMTS;Stage 2 functional specification of User Equipment    (UE) positioning in UTRAN (Release 15),” TS 25.305 V15.0.0, July    2018.-   [106] 3GPP, “E-UTRAN; Stage 2 functional specification of User    Equipment (UE) positioning in E-UTRAN (Release 15),” TS 36.305    V15.0.0, July 2019.-   [107] 3GPP, “E-UTRA; Requirements for support of radio resource    management (Release 15),” TS 36.133 V15.9.0, Jan. 2020.

What is claimed is:
 1. A method for selecting at least one location of (i) at least one receiver or transceiver or (ii) at least one transmitter or transceiver, the method comprising: facilitating a receipt, from the at least one transmitter or transceiver, of a plurality of signals by the at least one receiver or transceiver, wherein each of the signals has a multipath component; determining time of flight (ToF) information and angle of arrival (AoA) information of the multipath components present in the signals; determining one or more possible locations of (i) the at least one receiver or transceiver or (ii) the at least one transmitter or transceiver based on the ToF information, the AoA information, and a model of physical surroundings; and selecting the at least one location of (i) the at least one receiver or transceiver or (ii) the at least one transmitter or transceiver based on the one or more possible locations.
 2. The method of claim 1, wherein the plurality of signals are radiofrequency (RF) signals.
 3. The method of claim 2, wherein the RF signals are millimeter wave (mmWave) signals.
 4. The method of claim 1, wherein the plurality of signals are at least one of (i) acoustic signals, (i) audio signals, (iii) optical signals, or (iv) sonar signals.
 5. The method of claim 1, wherein the model of the physical surroundings is generated using at least one of: one or more video recordings of an environment obtained using a visible-light camera, one or more pictures of the environment obtained using the visible-light camera, one or more light detection and ranging (LIDAR) techniques to generate a 2D model or a 3D model of the environment, a millimeter wave (mmWave) radiofrequency (RF) radar, a computer-aided design (CAD) software application, a hand drawing, or floorplans or blueprints of a building.
 6. The method of claim 1, wherein at least one of the signals is provided at least one of (i) at a frequency in a range of approximately 6 GHz to 1 THz, or (ii) with a bandwidth of approximately 100 MHz to 10 GHz.
 7. The method of claim 1, wherein the determination of the one or more possible locations is performed by comparing at least one possible location with the ToF information and the AoA information.
 8. The method of claim 1, wherein the determination of the one or more possible locations is performed using at least one site-specific computer rendered simulation at least one of: in real-time, by a cloud server, on the at least one receiver or transceiver, or on the at least one transmitter or transceiver.
 9. The method of claim 11, wherein the at least one transmitter or transceiver is a base station and at least one receiver or transceiver is a cellphone.
 10. The method of claim 1, further comprising facilitating a cooperative localization as a function of the determination of at least one possible location.
 11. The method of claim 1, wherein the model of the physical surroundings is determined prior to facilitating the reception of at least one of the signals.
 12. The method of claim 1, wherein at least one of (i) the at least one receiver or transceiver or (ii) the at least one transmitter or transceiver is movable or fixed to a specified location.
 13. The method of claim 1, wherein the determination of the one or more possible locations is performed by a computer arrangement which is at least one of (i) a mobile system, (ii) a fixed system provided at a base station, or (iii) a cloud computing system.
 14. The method of claim 1, further comprising determining at least one of a position, a velocity or an acceleration of at least one of (i) the at least one receiver or transceiver or (ii) the at least one transmitter or transceiver using at least one of a Kalman filter, an extended Kalman filter, or a particle filter.
 15. The method of claim 1, wherein the selecting of the at least one location of the at least one receiver or transceiver is based on at least one of: a least-squares metric, or clustering the one or more possible locations and selecting a cluster containing a maximum number of the one or more possible locations.
 16. The method of claim 1, further comprising determining a carrier phase of at least one of the multipath components, wherein the carrier phase is used in conjunction with at least one of the AoA information or the ToF information of the multipath components to determine the at least one location of at least one of (i) the at least one receiver or transceiver or (ii) the at least one transmitter or transceiver.
 17. The method of claim 1, wherein the AoA information is determined using a phased antenna array provided at a location of at least one of (i) the at least one receiver or transceiver or (ii) the at least one transmitter or transceiver.
 18. The method of claim 1, further comprising, with onboard sensors, determining at least one of: an orientation of at least one of (i) the at least one receiver or transceiver or (ii) the at least one transmitter or transceiver, or z-coordinates of at least one of (i) the at least one receiver or transceiver or (ii) the at least one transmitter or transceiver
 19. The method of claim 18, wherein the onboard sensor which determines the orientation is at least one of a gyroscope or an accelerometer.
 20. The method of claim 18, wherein the onboard sensor used to determine the z-coordinates is a barometer.
 21. The method of claim 1, wherein the at least one location is selected based on a lookup table which includes the AoA information and the ToF information measured at a location calibrated in a surveyed environment.
 22. The method of claim 1, wherein at least one of the signals is at least one of (i) a pulsed signal, (ii) a signals that is spread over a bandwidth, or (iii) a signal that discretized over a plurality of individual frequencies.
 23. A non-transitory computer-accessible medium having stored thereon computer-executable instructions for selecting at least one location of (i) at least one receiver or transceiver or (ii) at least one transmitter or transceiver, wherein, when a computer arrangement executes the instructions, the computer arrangement is configured to perform procedures comprising: facilitating a receipt, from the at least one transmitter or transceiver, of a plurality of signals by the at least one receiver or transceiver, wherein each of the signals has a multipath component; determining time of flight (ToF) information and angle of arrival (AoA) information of the multipath components present in the signals; determining one or more possible locations of (i) the at least one receiver or transceiver or (ii) the at least one transmitter or transceiver based on the ToF information, the AoA information, and a model of physical surroundings; and selecting the at least one location of (i) the at least one receiver or transceiver or (ii) the at least one transmitter or transceiver based on the one or more possible locations.
 24. A system for selecting at least one location of (i) at least one receiver or transceiver or (ii) at least one transmitter or transceiver, wherein the at least one receiver is configured to receive, from the at least one transmitter or transceiver, of a plurality of signals, each of the signals having a multipath component, the system comprising: at least one processor which is configured to: determine time of flight (ToF) information and angle of arrival (AoA) information of the multipath components present in the signals, and determine one or more possible locations of (i) the at least one receiver or transceiver or (ii) the at least one transmitter or transceiver based on the ToF information, the AoA information, and a model of physical surroundings, wherein the at least one location of (i) the at least one receiver or transceiver or (ii) the at least one transmitter or transceiver is selectable based on the one or more possible locations. 